a proof of concept in the detection of pressure plate …
TRANSCRIPT
A PROOF OF CONCEPT IN THE DETECTION OF PRESSURE PLATE IEDS USING COMPTON BACKSCATTER IMAGING WITH FAN BEAM GEOMETRY
By
TRAVIS R. BARKER
A THESIS PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE
UNIVERSITY OF FLORIDA
2017
© 2017 Travis R. Barker
To the Soldiers I’ve been fortunate to fight alongside, the civilian volunteers who have shown us support, and my family for their unending patience; for SPC Jason Fingar,
1LT Rob Collins, 1LT Chris Goeke, 1LT Sal Corma, CPT Adam Snyder, and CPT Michael Kot
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ACKNOWLEDGMENTS
First, I’d like to thank Dr. Jim Baciak. I did not know that I wanted to be a nuclear
engineer until I spoke with him for the first time. I cannot thank him enough for steering
my academic pursuits into a field that will forever shape my U.S. Army and academic
career. In addition to introducing me to the world of nuclear security, his confidence in
my abilities and free reign to pursue this research served as a constant inspiration over
these past two years.
Likewise, I would like to acknowledge Gabriel Sandler and Shuang “Harry” Cui,
for their assistance in the laboratory and previous work with the equipment used in
experimentation. Additionally, none of my algorithm development would have been
possible without the support of Dr. Christopher Hughes, who helped me turn my
convoluted approach into a MATLAB reality and provided an honest sounding board
throughout my research at all hours. Furthermore, I would like to thank Kelsey
Stadnikia and Tyler Remedes for their help in study sessions throughout my master’s
classes, as I would not have succeeded in this research without their help learning the
basics of nuclear engineering.
I want to thank LTC Kwenton Kuhlman, LTC Neil Snyder, COL Joseph Power,
and COL Peter Benchoff. Throughout my time in the 101st Airborne Division, they
provided the constant mentorship and support that afforded me the opportunity to
succeed and eventually take on this Rendezvous with Destiny. I would never have
made it to the University of Florida without their support and recommendations.
Additionally, I would like to thank the United States Military Academy’s Physics and
Nuclear Engineering Department for affording my wife and I the opportunity to pursue
higher education.
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Finally, I want to thank my wife for dragging me, kicking and screaming, into an
assignment that has proven to be the best decision for our family and our sanity. I did
not realize how great of an opportunity this would be. The family time alone, to build
IED components with our son and get to know our little girl, has been a true blessing. I
am eternally grateful for her foresight, patience, and perseverance throughout this
adventure.
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TABLE OF CONTENTS
page
ACKNOWLEDGMENTS .................................................................................................. 4
LIST OF TABLES ............................................................................................................ 8
LIST OF FIGURES .......................................................................................................... 9
LIST OF ABBREVIATIONS ........................................................................................... 12
ABSTRACT ................................................................................................................... 13
CHAPTERS
1 INTRODUCTION .................................................................................................... 15
Overview ................................................................................................................. 15 Research Motivation ............................................................................................... 16
Compton Backscatter Imaging ................................................................................ 17 Cesium Iodide Detectors and Gadolinium Oxy-Sulfide Detectors ........................... 18
2 PRIOR WORK ........................................................................................................ 20
3 EXPERIMENTAL DESIGN ..................................................................................... 23
Pressure Plate Design Method ............................................................................... 23
Purpose ............................................................................................................ 23 Design Theory .................................................................................................. 23
Construction ..................................................................................................... 26 Further Testing Considerations ........................................................................ 29
Transmission System Design ................................................................................. 30
Backscatter System Design .................................................................................... 31 Calibration and Angle Maximization ........................................................................ 36
Other Tests for Image Resolution ..................................................................... 42 Initial CBI Testing ............................................................................................. 46
4 PROOF OF CONCEPT ........................................................................................... 49
Expected Results .................................................................................................... 49 Unobstructed Scans ............................................................................................... 50 Obstructed Scans ................................................................................................... 53
5 RESULTS AND ANALYSIS .................................................................................... 62
Grey Scale Observations ........................................................................................ 62 Grey Scale Analysis ................................................................................................ 63
Algorithm Conception.............................................................................................. 67
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Algorithm Noise Reduction ............................................................................... 69 Known Variable Definitions ............................................................................... 71 Initial Histogram Development .......................................................................... 73
Processed Results of the Algorithm ........................................................................ 74 True and False Positives ........................................................................................ 79 Obstructed Unmarked Tests ................................................................................... 83 Liesenfelt’s Cumulative Distribution Function and Streak Correction ...................... 85 Water Obstruction ................................................................................................... 90
Narrowed Source Collimator ................................................................................... 91
6 FUTURE WORK ..................................................................................................... 93
LIST OF REFERENCES ............................................................................................... 97
BIOGRAPHICAL SKETCH ............................................................................................ 99
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LIST OF TABLES
Table page 1-1 Properties of GOS vs CsI:Tl scintillators7 ........................................................... 18
2-1 Summary of Variables and Alterations ............................................................... 36
5-1 Median values of the grey scale analysis at each dip and peak for the CsI:Tl detector. ............................................................................................................. 66
5-2 Example break down of data points forced into the three original histogram bins, the “Forced Algorithm” ............................................................................... 73
5-3 Example break down of data points using the final histogram bins, the “Quaternary Algorithm” using a set standard deviation of 500 ............................ 74
5-4 Consolidated Results of false positive and false negative comparison ............... 83
5-5 Consolidated Results Contrasting Copper Histogram Results ........................... 84
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LIST OF FIGURES
Figure page 1-1 Pixel Matrix ......................................................................................................... 18
1-2 X-Scan CsI:Tl linear detector array ..................................................................... 19
2-1 Antipersonnel mine at depths of 2 cm and 4 cm imaged .................................... 20
2-2 Push Broom Design ............................................................................................ 22
3-1 Design Version 1.0 ............................................................................................. 26
3-2 Pressure Plate Boards, Dowel Stabilizers, and Conical Springs ........................ 27
3-3 Interior Plate Design ........................................................................................... 28
3-4 Completed pressure plate prototype ................................................................... 29
3-5 Diagram of transmission scan system. ............................................................... 30
3-6 X-ray transmission image of the pressure plate.................................................. 31
3-7 Image of the Lead plate collimator ...................................................................... 31
3-8 Diagram of Backscatter system. ......................................................................... 32
3-9 Depiction of the detector collimator septa and spacers ...................................... 33
3-10 Example of lateral migration’s effect on pixel location ........................................ 33
3-11 The Black (R) and Gold (L) collimators ............................................................... 34
3-12 Raw image data of integration times .................................................................. 37
3-13 Calibration curves ............................................................................................... 38
3-14 Image of the lead test strips ............................................................................... 39
3-15 Angle Comparison for the CsI:Tl detector ........................................................... 40
3-16 Ideal Angle comparison between CsI:Tl and GOS detectors .............................. 41
3-17 Comparison of power scans from the CsI:Tl detector ......................................... 41
3-18 Diagram of Initial target area .............................................................................. 42
3-19 Scan results from CsI:Tl detector of perpendicualar shimmies ........................... 43
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3-20 Wire spacing scan results image and corresponding photograph ...................... 43
3-21 Liesenfelt’s rasting with 1mm resolution ............................................................. 44
3-22 Scan results of UF image scan ........................................................................... 45
3-23 Diagram of the Pressure plate set up ................................................................. 47
3-24 Diagram depicting sand scans with and without markers ................................... 48
4-1 Diagram depicting expected results .................................................................... 49
4-2 GOS detector calibration curves ......................................................................... 50
4-3 Initial scan results with the GOS detector ........................................................... 51
4-4 Final unobstructed image for the GOS detector ................................................. 51
4-5 GOS (B) and CsI:Tl (T) Calibration curves ......................................................... 52
4-6 Sand scan diagram ............................................................................................. 54
4-7 Obstructed diagram and side picture with lead markers ..................................... 54
4-8 Top view of the scan area .................................................................................. 55
4-9 New Angle Maximization for GOS detector at 3.75 cm ....................................... 57
4-10 New angle maximization for the CsI:Tl detector when raised 3.75 cm ............... 57
4-11 Raised 3.75 cm scan image of obstructed scan taken ....................................... 58
4-12 GOS scan at original height with two lead markers ............................................ 58
4-13 Final sand GOS obstructed scan with markers................................................... 59
4-14 Final sand CsI:Tl obstructed scan with markers ................................................. 59
4-15 Water bag obstruction experimental design ....................................................... 60
4-16 Water obstructed scan results with the CsI:Tl detector ....................................... 60
5-1 Scan results of a lead shimmy ............................................................................ 62
5-2 GOS detector unobstructed scans contrast adjusted ......................................... 63
5-3 Grey scale graph of a unobstructed CsI:Tl scan ................................................. 64
5-4 Grey Scale GOS image aligned with the pressure plate ..................................... 65
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5-5 Grey Scale CsI:Tl image aligned with the pressure plate ................................... 66
5-6 MS Excel heat mapping ...................................................................................... 67
5-7 First 30 data points on pixel 96 averaged ........................................................... 69
5-8 Calibration curve for the CsI:Tl detector ............................................................. 70
5-9 Background noise ............................................................................................... 70
5-10 Construction of the copper wire sample ............................................................. 71
5-11 Known material samples .................................................................................... 72
5-12 Original transmission scan ................................................................................. 74
5-13 Final obstructed GOS and CsI:Tl scans with markers ........................................ 75
5-14 CsI:Tl obstructed scan with the "forced" algorithm. ............................................ 76
5-15 GOS and CsI:Tl contour plots using the "quaternary" algorithm with ±𝟎. 𝟓𝟎𝝈. ... 77
5-16 GOS and CsI:Tl contour plots using the "quaternary" algorithm with ±𝝈. ........... 78
5-17 2D side view of the target area after processing CsI:Tl obstructed scan ............ 79
5-18 MPB False Positive Test experimental setup. .................................................... 81
5-19 False positive scan image data .......................................................................... 82
5-20 Obstructed scans without the lead markers both from both detectors ................ 83
5-21 Processed Results of the CsI:Tl detector Obstructed Unmarked Scan .............. 84
5-22 Processed image results of a CsI:Tl obstructed scan with no markers ............... 86
5-23 Processed results of a GOS obstructed scan with no markers ........................... 87
5-24 Perpendicular pressure plate obstructed scan diagram ...................................... 88
5-25 Final Obstructed Scan with no markers using the CsI:Tl .................................... 88
5-26 Processed results of obstructed perpendicular scan .......................................... 89
5-27 Processed image of obstructed pressure plate under water ............................... 90
5-28 True image data from the Narrowed Collimator source ...................................... 92
6-1 Faster table speed with the same integration time as previous scans. ............... 94
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LIST OF ABBREVIATIONS
CBI Compton Backscatter Imaging – The use of photons emitted during X-ray scans in the mapping of a surface.
CIBR Computed Image Backscatter Radiography – Similar to RSD, but several orders of magnitude faster. It uses a fan beam and a series of detectors to determine the scatter location based on the composite data collected by a series of detectors.
EOD Explosive Ordinance Disposal – Military Bomb Squads. Responsible for the interrogation, forensics, removal, and destruction of mines, unexploded ordinance, and IEDs.
GPR Ground Penetrating Radar – The current detection method used by US and coalition forces in the detection of land mines and IEDs. Uses low frequency radar to detect the presence of mines and UXOs.
IED Improvised Explosive Device – Bombs used by insurgent forces in warfare, in which the components to include primary explosive, detonating explosive, power source, and trigger mechanism are improvised from seemingly unrelated parts.
JIEDDO Joint Improvised Explosive Detection Defeat Organization
MPB Manufactured Particle Board – Standard particle board made from the dust remains in a wood factory. The highly randomized internal structures serve as a good calibration medium for the detectors.
MWD Military Working Dogs – Specially trained dogs used for either the detection of explosives and drugs, security of their handlers, and capture of enemy combatants or combination thereof.
RSD Radiography by Selective Determination – Early CBI method that uses a collimated detector, non-collimated detector, and pencil beam source to perform CBI. Highly accurate, but generally slow.
𝝈 Sigma – Greek symbol representing standard deviation.
UXO Unexploded Ordinance – Bombs, bullets, or mines that either did not detonate as they were intended to (duds) or were removed from their original placement (such as a mine field). These provide the explosive capabilities for some IEDs.
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Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the
Requirements for a Master of Science
A PROOF OF CONCEPT IN THE DETECTION OF PRESSURE PLATE IEDS USING COMPTON BACKSCATTER IMAGING WITH FAN BEAM GEOMETRY
By
Travis R. Barker
May 2017
Chair: James Edward Baciak Major: Nuclear Engineering Sciences
Current land mine and Improvised Explosive Device (IEDs) detection methods
predominately rely on the use of ground penetrating radar and metal detectors to scan
the ground for disturbances in electromagnetic waves that would indicate a higher
concentration of metal. Combined with trained observational skills, soldiers use these
tools to detect the presence of IEDs, meaning sometimes the detection of IEDs is more
of an art than an exact science. A portion of these detection methods are taught and
trained to every soldier, as the principles behind IED detection are similar no matter the
IED construction. Specifically, all IEDs require the use of wires in some way.
This research has expanded the use of Compton Backscattering detection
methods and pencil beam imaging, proven to be slow, but accurate. Using fan beam
imaging instead, has allowed for a rapid and wider scan of potential pressure plates
focused on detecting the signature of a wire in a typical pressure plate IED design.
Through both raw data and simplistic algorithms, this research has shown an increase
the accuracy over current interrogation methods through a non-destructive means.
Existing interrogation methods leave a unit exposed, with every second favoring an
opportunistic enemy, and an increased potential for premature detonation. Ultimately,
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this could allow units the ability to clear suspected enemy obstacles at a faster rate of
speed, with increased accuracy, and providing greater security to the soldiers on the
ground.
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CHAPTER 1 INTRODUCTION
Overview
Improvised Explosive Devices (IEDs) have caused more damage in the global
war on terror than any other weapon in an insurgent’s arsenal. They require very little
oversight depending upon their construction, are systematically simplistic, and create a
significant amount of chaos with little exposure to the insurgent forces that employ
them. Their effects however, are relatively indiscriminate. Not only can they harm
civilians on the battlefield, but also they maim, disable, or kill with little difference to the
victim or vehicle they affect.
The purpose of this research is to develop a proof of concept in the use of a non-
destructive, non-intrusive detection method of IEDs using Compton Backscatter Imaging
(CBI) in a fan-beam configuration. Compton Backscatter Imaging is a single sided
imaging method that uses X-rays to distinguish between materials based on the
radiation signature that bounces back from the irradiated medium. This method of
imaging has proven useful in railroad tie inspection, luggage and port security
inspections, and aerospace and missile applications as well. Furthermore, as early as
1973, it has shown potential for use in land mine detection through the use of pencil
beam imaging. Although highly accurate, the loss of speed in order to obtain higher
image resolution has previously made CBI a less than ideal method of land mine
detection. However, given advances in CBI techniques, the potential for using CBI for
interrogation of a suspected device exist. This creates the potential to decrease the
exposure of friendly soldiers on the battlefield.
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Research Motivation
Since the war on terror began in Afghanistan 15 years ago, IEDs have become
the weapon of choice for insurgent groups, resulting in a transformation of coalition and
US tactics in order to combat this threat. Route Clearance Patrols, consisting of
Combat Engineers, Explosive Ordinance Disposal (EOD), and security elements
conduct daily patrols along major movement routes to detect and dispose of these
threats. Detection predominately relies on Ground Penetrating Radar (GPR), metal
detectors that use electromagnetic induction (EMI), military working dogs (MWDs) and
visual ques.3, 12, 13 GPR does allow for rapid detection at higher speeds for vehicular
movement, but sometimes the signal can be unclear, giving a false positive. As a
result, using other visual cues observed by soldiers prove helpful in the detection of
IEDs. For every soldier, these visual cues are simple observations like lines in the
sand, disturbed dirt, or trash that seems out of place. Many of these indicators of IEDs
can be foiled by time, patience on the part of the insurgent, and weather effects, such
as heavy rain, that can dampen signals and disguise visual clues that soldiers may use
to identify a threat. After detection, the interrogation of IEDs is a lengthy process as
well.
During the interrogation process, EOD and engineers must first ensure a
suspected IED is in fact an IED before documenting, obtaining evidence for follow on
forensics, and eventually destroying or disabling the device. Depending upon how the
IED is camouflaged, this can take anywhere between a couple of minutes to a half an
hour of interrogation. Engineers may poke the IED with non-magnetic rods, scrape it
with their robotic arms, or spray compressed air to clear any mud or sand that may be
covering the device or its trigger mechanism. This interrogation processes exposes the
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unit to ambushes, especially if the IED is a decoy designed to halt a convoy.
Additionally, these delays have resulted in commanders assuming risks on the
battlefield when time is a pressing concern; sometimes bypassing clearance units in
order to complete urgent missions.
Although CBI has proven effective, but slow at land mine identification, it has the
potential to be incredibly effective for land mine interrogation. On the battlefield,
seconds add up to minutes, which can allow the enemy the freedom to maneuver
offensively or defensively. The method taught to soldiers deploying overseas to current
theaters of operation in Afghanistan or Iraq use a simplistic approach of looking for
what’s out of place. Applying this approach to CBI has the potential to cut minutes to
seconds in the interrogation process, thereby saving lives on the battlefield. Rather
than detection, the aim of this research is develop a method of finding the circuitry wire--
the material that is out of place.
Compton Backscatter Imaging
Research in Compton Backscatter Imaging has resulted in a variety of methods
to ensure clarity of image resolution, while reducing dose and acquisition time.
Radiography by Selective Determination (RSD) is a CBI method, which uses a
collimated detector, non-collimated detector, and a pencil beam source to subtract noise
from the signal output of the scanned target in order to obtain the greatest possible
clarity of the target’s image. However, the challenge with RSD is that it is slow in
acquisition time due to the methodical nature of scanning, or rastering, the target in a
matrix pattern.
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Figure 1-1. Pixel Matrix as depicted by Chris Meng10
For example, using a 192 pixel size similar to the Cesium Iodide detector used in
this research, would require a scan at each of the 192 pixel locations. Additionally,
since each pixel of the scanner is 800 microns or 0.08 cm, to do a 30 by 15.36 cm
target area requires scanning 5,760 pixel locations. For the Gadolinium Oxy-Sulfide
detector that was used with 2038 pixels measuring 100 micron each, the same target
area requires scanning 46,080 individual points. This is best demonstrated in Figure 1-
1.
Cesium Iodide Detectors and Gadolinium Oxy-Sulfide Detectors
The detectors used for this experiment were both X-Scan Line Scan Camera
detectors shown in Figure 1-2. These detectors offer the ability to consolidate photon
absorption and light emission from the pixels and transfer the information in to a digital
or analog reading that can then be compiled as an image with the same functionality of
a camera. The scintillating material used by the two detectors were Thallium doped
Cesium Iodide (CsI:Tl) and Gadolinium Oxy-sulfide (GOS).
Table 1-1 Properties of GOS vs CsI:Tl scintillators7 Material Density(g/cm3) Effective Z Light Yield (photons/MeV) Decay time (ns)
GOS 7.34 64 60,000 6 x 105 CsI:Tl 4.52 54 66,000 6 x 103
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GOS and CsI:Tl scintillators have a similar light yield, with CsI:Tl yielding 10%
more than GOS scintillators; however, the slightly hygroscopic nature of CsI:Tl could
present a challenge for use in harsh environments that military forces are sometimes
deployed to. X-Scan’s CsI:Tl and GOS detectors are designed to survive rapid
accelerations and stops and are hardened to protect the detector, but ultimately, more
hardening methods would have to be employed in order to ensure that either detector is
protected from all potential environmental exposure.
Figure 1-2. X-Scan CsI:Tl linear detector array. Photo courtesy of Travis Barker.
In selecting the appropriate detector for this project, both detectors were in use at
the University of Florida for other projects; and although neither was chosen prior to the
project as the ideal detector for all experiments, both detectors proved adequate for a
proof of concept.
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CHAPTER 2 PRIOR WORK
Since 1973, multiple research efforts have studied the potential for using
backscatter imaging for detection of landmines, primarily because of the challenges
presented by plastic mines.6, 8, 11 The primary challenge with landmine detection,
particularly current landmine designs, is the low metallic signature reduces the
detectability both for CBI and Ground Penetrating Radar. In A Survey of Landmine
Detection Technology, Dr. L. Robledo presented the alternatives often used for
detection in both a military and civilian standpoint. The conclusion he came to was that
for detection and interrogation, a combination of sensors provides the greatest
awareness in the removal of landmines.13
Figure 2-1. Antipersonnel mine at depths of 2 cm and 4 cm imaged through Niemann’s pencil beam techniques11
Dr. Edward Dugan and students at the University of Florida, expanded rasting
techniques central to Compton Backscatter Imaging and demonstrated that not only is
land mine detection possible through this method, but land mine identification as well.
However, despite the incredible accuracy, speed of detection still provided a significant
challenge. Testing using a pencil beam source proved successful with the most recent
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success demonstrated by Dr. Wilhelm Niemann et al, with both anti-personnel and anti-
vehicular mines as shown in Figure 2-1.10
Dr. G. Harding and Dr. Hussein found similar results to Dr. Niemann’s work
presented in Fig. 2-1, including detection at depths as high as 10 cm.5, 6 However, Dr.
Harding also determined time to be a limiting factor in making detection a reality,
especially given the limitations of pencil beam sources.5 Dr. Sunwoo Yuk produced
similar results using a plastic mine, with a pencil beam imaging system and an
integration time less than 1000ms. Finally, researchers in Japan, working with the
Tokyo Denki University attempted a similar endeavor using a combined neutron and
gamma detection system. The results of which produced a detection method using 2-5
MeV photons to image plastic mines.14
In 2008, Major Chris Meng under the advisement of Dr. Dugan expanded upon
the field of CBI and RSD through the development and testing of Computed Image
Backscatter Radiography (CIBR).10 The method behind CIBR is to use a fan beam
source to irradiate a target area. The resulting scatters are then mapped through the
data reconstruction of four collimated Yttrium Sulfur Oxide (YSO) detectors. By
contrasting the data of the four detectors, he was able to construct an image of a target
area with increased speed, resulting in a time difference of approximately 250 minutes
and an order of magnitude less current than required for RSD, for a total acquisition
time of 7.8 minutes.
Most recently, Dr. Jessica Kelley in conjunction with Georgetown Rail Equipment
demonstrated with a push-broom design, the ability of using fan beam geometry with a
linear detector to detect deterioration in railroad cross ties demonstrated in Figure 2-2.
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With her design, the detector and source array could detect large-scale deterioration in
cross ties while moving at speeds in from 10 to 30 mph. The focus of Dr. Kelley’s scans
was to aim at air voids or vacancies 18-20 cm below the surface scan.8
Figure 2-2. Push Broom Design developed by Dr. Jessica Kelley and Georgetown Rail Equipment to scan for deterioration in railroad cross ties8
Previous landmine detection efforts looked for these voids as well, as these voids
indicated low-density interior air volumes within the mines.8 Although unrelated to land
mine detection, Dr. Kelley’s efforts unintentionally demonstrated a potential use of CBI
technology in land mine detection. The speed at which she acquires data and
discriminates air pockets in rail ties does present a combat multiplier for land mine
detection. By utilizing a similar experimental design, it might be possible for CBI to
replace GPR as the primary land mine detection method.
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CHAPTER 3 EXPERIMENTAL DESIGN
Pressure Plate Design Method
Purpose
In order to create the most realistic experiments in terms of real world
application, the target for backscatter radiography scans is a pressure plate system. Its’
design was constructed similar to the training example taught by the United States
Government Joint Improvised Explosive Device Defeat Organization (JIEDDO), to all
soldiers entering Afghanistan.
Design Theory
The mechanics and operation of a pressure plate, remote controlled or command
wire IED, are similar and simplistic in nature. Due to a limited abundance of
professionally designed explosive materials and components, the most important factor
for any portion of the design is simplicity and cost effectiveness. The four major
components include a power source, the initiator/detonator, the primary explosive/main
charge, and a switch.7
In order to get a rapid explosion, a power source is required to fuel the reaction.
Although chemicals can be explosive on their own when mixed properly, a rapid and
timely capability requires a power source to ensure controlled operation. The power
source is typically a battery, though the size can range from a 9V battery, seemingly
“dead” AA batteries in series, or even a motorcycle battery. From a detection
standpoint, simply searching for a power source is inefficient and has a propensity to
produce false positives. Furthermore, the insurgent may remove or offset the power
source from the area for repeated uses.
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The initiator is a small explosive device designed to detonate the primary
explosive. Typically, the initiator is stable until a charge or pressure is applied. For
conventional blasting caps, this charge is typically electric; even static discharge can be
enough to detonate the initiator, resulting in a small explosion with a yield similar to
commercially purchased fireworks. Once detonated, the initiator will cause the primary
explosive charge to detonate. From a detection standpoint, initiators are always within
the primary explosive, and thus relatively inefficient to detect when compared to the
larger main charge.
The main charge is comparatively stable, though once detonated by the initiator,
yields a larger explosion. Historically, the main charge composition for IEDs have
consisted of ammonium nitrate fertilizer or urea fertilizer; however, some insurgents
have used explosives from military made mines or bombs, sometimes making use of
the existing shells for added shrapnel and protection for the charge. Sometimes
insurgents will attach additional shrapnel or flammable accelerant to the main charge for
added effects, but that is not always the case and is dependent upon the intended
target of the IED’s design. Main charges are what GPR primarily detects; however,
water, mud, or other debris may confuse the signals.3, 13
The final piece of an IED is the switch. In order for the device to work, the power
source must make a complete circuit with the detonator. Once the circuit is complete,
the detonator will ignite the primary explosive, with explosive results that are designed
to kill, disable, or maim the intended targets. As a result, even though the explosive
material and detonator change in terms of composition, and power sources can vary
based on available materials, the switch must have some sort of wired connection to
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complete the circuit. Without the ability to complete the circuit, the power source will
never be able to ignite the detonator.
The switch comes in a variety of forms, and it can be victim operated or
command operated. In the case of a command-operated device, a radio, cell phone,
clock, timer, car key remote, or other various remotes can all serve the purpose of
activating a device remotely. Defeating these devices is simplistic enough with signal
jammers, which are accessible to every unit, whether the US soldiers are on a
dismounted or mounted patrol. As a result, US Soldiers see fewer and fewer of these
devices thanks to our Counter-IED (CIED) devices. However, wired devices or victim-
operated devices continue to pose a realistic threat.
Wired command operated devices and victim operated devices share a
commonality in that they are simple to build and difficult to defeat unless they are
detected. The command wire devices depend upon the IED operator to simply connect
a wire to the battery power supply. Victim operated devices however, are the result of
the intended target completing the circuit unknowingly. In the case of a trip wire, the
victim releases a restricting tension that allows the circuit to connect. In the case of
pressure plates, the victim forces two pieces of metal together, thus creating a complete
circuit. In either case, the completion of the circuit, like a conventional light switch, is
the final act that must occur for the device to detonate. This theory of operation applies
for both vehicle and personnel targets alike.
Given that pressure plates pose a significant threat in CIED operations, the
purpose of this experiment is to focus on the properties of a pressure plate. As a result,
the design that is being utilized is a common one depicted in JIEDDO briefings to US
26
soldiers before they deploy.7 The design includes two wooden plates, four conical
springs, electrical tape, and wooden spacers. Because of the potential for varying sizes
and shapes of wire in a real world application, multiple versions of the pressure plate
could be built, with identical building materials and dimensions. The exception being
that other models could use 14g or 16g wire, stranded wire instead of solid wire, or even
steel saw blades instead of the 12g wire the prototype will use. Additionally, almost
every IED system utilizes wires in some fashion for the electric current. If this detection
method works for pressure plates, it may also aid in the detection of command wires as
well.
Construction
The original design, version 1.0, is shown in Figure 3-1. Upon purchasing
materials, several design modifications became necessary, but with no detriment to the
operation of the design, nor did they deviate from the design theory. For example, the
spacers depicted in Figure 3-1 for upper and lower crush plates were modified to
include only one spacer, with the second wire flush against the lower pressure plate.
Figure 3-1. Design Version 1.0
6.3 cm
30 cm
1.3 cm
1.9 cm
1.9 cm
0.6 cm8.9 cm
crush plates
spacer
conical spring12g, 14g,
16g wire
Pressure Plate Design v1.0Compton Backscatter IED Detection ProjectTravis R. BarkerCreated 25 FEB 16
27
The first modification was the overall length of the pressure plate because of the
materials available. Compared to Figure 3-1, the basal wood spacers were only 22.8
cm (9 in) long, thus 7.2 cm (3 in) was removed from the over-all length of the entire
length. After adjusting the length of the plates, the base of the conical springs (1.9cm
lower diameter, 0.95 cm upper diameter, 2.54 cm uncompressed height) were used to
adjust the interior spring locations. Ultimately, this has no effect on the pressure plate’s
operation, only on the size of the target that will be scanned in later testing.
Figure 3-2. Pressure Plate Boards, Dowel Stabilizers, and Conical Springs. Photo courtesy of Travis Barker.
The final position for the center of the spring base was 2.54 cm from the ends,
and set 1.9 cm inwards from the edges. The springs were installed by using a forstner
bit to sink each spring in to the pressure plates approximately 0.5 cm before using hot
glue to affix the spring to the pressure plate hole. For the prototype, there are four
dowel spacers, each 5 cm long, to act as guides in the event the springs became
unstable during the pressure plate’s compression, to help with repeated compressions;
however, these proved to be redundant as the springs were stable enough. When
building follow on versions of the pressure plate design, the dowels are easily
28
attachable should they become necessary. The stabilizing dowels and springs can be
seen in their proper configuration in Figure 3-3.
Figure 3-3. Interior Plate Design. Photo courtesy of Travis Barker.
Once the stabilizers and springs were in place, a basal wood spacer was added
and hot glued it to the upper crush plate. Throughout the design, every effort was made
to remove the addition of metal to the design for two reasons. First, the addition of
metal may make the pressure plate more easily detectable by traditional metal detector
means. Second, the focus of the scans is on the signature created by the copper wire,
not the steel springs or staples. In actuality, the springs, which serve as spacers to
prevent a complete circuit, are not necessary, because any ductile, malleable, or
squishy substance that provides spacing will work. It is important to remember that the
design characteristics of an IED are truly improvised.7
To fit the wires to the crush plate and basal wood spacer, a Dremel tool was
used to allowed for the creation of a slight groove in the basal wood spacer for the
straight portion of the wire to ensure lateral stability of the upper wire. On the lower
wire, to increase the probability of contact when compressed, a serpentine or sinusoidal
pattern with the wire. Despite best efforts to remove metal from the design, wood
29
staples were necessary to hold the wire in place. When originally tested during
construction, the 12 gauge solid wire was not malleable enough to ensure good contact
with the lower crush plate notwithstanding the use of hot glue at each of the vertices of
the serpentine shape. This caused the circuit to maintain a current even when pressure
was removed from the switch.
Once all wires were affixed, the plates were connected using the springs and
stabilizing dowels. Electrical tape was added to ensure even if the dowels fail, the
boards would remain in their proper configuration. The final configuration is shown in
Figure 3-4. The final circuit test was conducted using a voltmeter, and which yielded a
resistivity measured at approximately 1 ohm.
Figure 3-4. Completed pressure plate prototype using 12g stripped solid copper wire. Photo Courtesy of Travis Barker
Further Testing Considerations
Overall, the final design meets all necessary requirements described in the
design theory. It is simplistic in nature; all of the materials are easily procured at a
hardware store, or could be fabricated with scrap materials.7 It completes an electrical
circuit when compressed, thus producing the necessary conditions to detonate an
explosive initiator if given a power source. From here, the next step is designing the
test platform; to include the sand material the pressure plate will rest in.
30
Not all pressure plates are covered in sand or soft dirt, as some are placed in
buildings under rugs, loose boards, or in a doorway. Given the size of the detector,
building a design that could be man portable to address those threats is not possible at
this time. Finally, in order to simulate a lightly buried IEDs, the experimental design will
utilize 3.81 cm of sand. This is 0.2 cm less than the maximum depth used by Niemann
et al. for their pencil beam methods; however, it should adequately address a realistic
depth for a “lightly buried pressure plate” device.
Transmission System Design
For the purpose of this experiment, a modified version of the push-broom design
used by Dr. Jessica Kelley is used.8 The Comet 451RX X-Ray tube has a 450 kV max
voltage with a 4.1 mA maximum current, and a max power output of 700W. For the
initial testing of the detector and the pressure plate design, a transmission scan was
performed using the set up depicted in Figure 3-5.
Figure 3-5. Diagram of transmission scan system. Scanner (1), Movement Table (2), Pressure Plate (3), Fan Beam X-ray (4), X-ray Tube (5).
31
Using the design depicted in Figure 3-5, the pressure plate was imaged with no
collimators in order to identify the targeted wire was in fact visible using the linear
detector arrays. As shown in Figure 3-6, the wire presents a darkened line for the GOS
detector with an 800 micron resolution. Although the transmission scans provide a
large amount of details as to the wire’s shape, it is important to note that clarity for the
CBI scans would not be as high based on the scattering of photons both going through
the sand to the target area, and out of the sand to the detector.
Figure 3-6. X-ray transmission image of the pressure plate under 1.5 in of sand using the GOS detector.
Backscatter System Design
For CBI imaging, the source beam was collimated to a fan beam using a series
of adjustable lead plates and lead plug as shown in Figure 3-7. These allowed for an
adjustable fan beam, which could alter resolution during later testing. The initial beam
source collimation was 0.95 cm by 10 cm.
Figure 3-7. Image of the Lead plate collimator with corresponding lead plug collimator both in (R) and out of the source tube (L). Photo courtesy of Travis Barker
32
The radiation from the tube was oriented at a scan table with a lead base plate
on top of a plywood board for stability, with a series of wood manufactured particle
boards (MPB) that could be manipulated depending upon the experimental needs.
Given the variety of experiments used for angle maximization, concept testing, and
algorithm development, the layers of MPB proved beneficial to standardizing the
calibration scans for the detector. By having a uniform density surface at the same
distance from the CBI detector for every test, the light and dark calibrations of the
detector remained constant throughout the experiment.
An aluminum scan table was developed to move mono-directionally laterally
through the scan field, allowing for a stationary detector and source tube. Although this
setup could change in a field concept to a push-broom style prototype, for the purposes
of proof of concept, allowing the table to move the target of interest provided the most
control over movement speed, height and angle of the scan as depicted in Figure 3-8.
Figure 3-8. Diagram of Backscatter system. Detector (1), Scan Table (2), Pb Baseplate (3), X-Rays (4), Source Tube (5), Backscatter Photons (6)
Finally, after impacting the scan table, the x-rays would ideally bounce back to
the detector. Both the CsI:Tl detector and the GOS detector utilized collimators
developed in previous CBI projects. These collimators utilized lead septa spaced out
33
with basal wood stabilizers that are perpendicular to the detector inlet. Depending upon
the amount of power used for the experiment, the amount of detail desired in the image
and the sensitivity of the detector, each of the previously developed collimators provided
different results, which were analyzed in the detector angle maximization experiments.
Figure 3-9. Depiction of the detector collimator septa and spacers, demonstrating the noise reduction and conical nature of fan beam CBI
As shown in Figure 3-9 and Figure 3-10, the spacing between the septa can
either increase or decrease the amount of photons that enter the detector’s opening slit.
As a result, collimators with a more lead septas reduce the amount of photons that
interact with the scintillators and darken the image. To prevent lateral migration,
collimation is needed to ensure that the photons that impact the area directly under
each pixel strike the intended pixel, and not adjacent pixels.
Figure 3-10. Example of lateral migration’s effect on pixel location with (1) and without collimation (2), and the effects of lateral migration (3,4)
34
The collimators developed by Cui and Sandler were wrapped in gold, green, and
black duct tape to identify them based on the spacing between septa. The green
collimator was originally tested, but discarded because it did not fully cover the entire
opening slit on the GOS or CsI:Tl detector. The gold collimator had a spacing of 0.63
cm between the septa; with a total septa count of 42 or in other words, one septa for
every seven to eight pixels of the CsI:Tl detector or every 63 pixels of the GOS detector.
The black collimator had much more densely packed septa, with a spacing of 0.079 cm
between the septa, with a total septa count of 336, or one septa for every one pixels for
the CsI:Tl detector or every seven pixels of the GOS detector. Figure 3-11 shows both
collimators, demonstrating the internal septa spacing and the collimator in its entirety.
Figure 3-11. The Black (R) and Gold (L) collimators both with and without their external wrapping. Photo courtesy of Travis Barker
The importance of this is for either detector, the black collimator limited the signal
so much, it required more power or photons in order to generate a useful image of the
pressure plate. For the CsI:Tl detector, this could be a disqualifying property as the
35
number of septa almost prevent any signal from reaching the 800 micron pixels;
therefore, dampening the detector to the point of making it unusable. For the GOS
detector, it didn’t cause as much of an issue due to the quantity of 100 micron pixels.
However, in either case increasing the power would reduce the feasibility of developing
a field worthy prototype based on a Comet 300Ds x-ray tube specifications.2 Comet
offers man portable x-ray tubes that could easily work for prototype development even if
mounted on a vehicular arm, but with a max voltage of 300kV and a max power of
900W.
To ensure experimental integrity throughout the testing, the following variables
were treated as constants in the experimental design in order to systematically test their
impacts on the scanned targets. The source tube remained at the same height
throughout the entirety of experiments in order to ensure a constant point of reference
for experiment height, detector position, incident angle of the radiation, and the
scanning length. The distance from the tube to the target area was only altered when
needed by raising the height of the scan table in the experiment. The height of detector
and distance from the tube remained constant as well.
Although increasing the distance from the detector and x-ray tube to the target
would allow for a larger scan area based on the conical nature of fan beam geometry, it
would also increase the Signal to Noise Ratio (SNR) from background radiation. The
result of which would be a greater need for collimation of the detector. As experiments
proved during the project, excessive collimation resulted in a decrease in brightness
and contrast, further reducing resolution. All of these variables and constants are
summarized in Table 2-1.
36
Table 2-1. Summary of Variables and Alterations Variable/Constant Alterations Possible Purpose
Source Tube Height (Constant)
Not Raised/Lowered Served as a point of reference throughout the experiment.
Detector Position (Constant)
Remained Stationary Served as a point of reference throughout the experiment.
Detector Angle (Variable)
Rotated about central axis Allow for maximum detector sensitivity.
Table Height (Variable) Raised and Lowered Maintained system integrity versus moving the detector or the source tube. Done for image clarity
tests. Table Speed (Variable) Increased and Decreased Allow for acquisition speed tests.
Integration Time (Variable)
Increase or decrease lines per second
Increase and decrease resolution, or decrease acquisition time. Later testing proved unsuccessful.
Calibration and Angle Maximization
Image quality with the linear detector array is contingent on reducing the SNR.
Although collimation is fundamental to managing the noise of stray photons, maximizing
the window for ideally scattered photons is necessary too. Additionally, the decay time
of the scintillator determines how quickly a pulse can be measured. As a result, in order
to obtain an accurate image from CBI, determining the ideal integration time and angle
of incidence from the fan beam source to the pressure plate and back to the detector is
crucial.
The integration time is the time it takes for each pixel to receive a pulse from the
photons in the target area, turn that pulse into an electric signal, then return to an
unexcited state in order to receive the next pulse. Setting the integration times to
smaller values would potentially allow a faster scan to occur due to the detectors ability
to recognize changes in a shorter amount of time. The tradeoff is that it creates less
manageable results in terms of image quality, as well as data processing time because
of an increase in electronic noise with the given detectors. With the linear detector
arrays, an image grey scale serves as the data, where the brighter the pixel energy
value on the grey scale directly correlates to the higher scattering the material.
37
Conversely, if the integration time were set too large, the detector data would
take so long to acquire that it would not meet the operational objectives of a rapid
means of identifying pressure plate devices. Additionally, the software provided by X-
Scan with both the GOS and CsI:Tl detector struggled to produce results or a calibration
with larger integration times. As shown in Figure 3-12, where the raw data for turning
on the x-ray source seems to have little contrast compared to the majority of the
experimental scans used in this research. Although the default setting on the CsI:Tl
detector was 3.20x10-4 s, the ideal integration time after testing the data output and
calibration curve was 0.032 s. This equates to 30 scanned lines per second, or
approximately 0.225 cm/s. If the scan table moved at the same rate as the detector
integration time, each pixel would correspond to the same scan area of approximately
6.75 cm.
Figure 3-12. Raw image data of short integration time (T) as well as a lengthy integration time (B) during initial x-ray activation in a test scan.
Given the significant power output of the X-ray source tube, it was possible to
create a strong fan beam source for scanning. However, during initial experimentation
with the detectors, the minimum voltage and current needed to obtain a clear image
were determined experimentally. Although a 400 kV at 4.0 mA scan could give a clear
image, it also washed out the detector. Conversely, a 160 kV at 3.0 mA scan yielded
38
little contrast, and the majority of the signal washed out by noise, yielding an image akin
to television static.
As a result, the calibration for ideal angle was performed in conjunction with a
power setting of 300 kV at 3 mA as the maximum power setting for the CsI:Tl detector.
This value was determined using the maximum output of a Comet 300DS source tube,
as using this existing model specifications could conceivably allow for a future prototype
development based on its portability.2 After determining the ideal angle, test scans
were performed to verify that 300 kV with 3 mA was the ideal power output for the CsI:Tl
detector.
Initially, the x-ray tube was oriented towards the front of scan table track, thereby
bouncing the x-rays off the target area, upwards towards the detector. Using a small
angle of incidence of approximately 6 degrees, it ensured that the scan table had
enough room to move through the entirety of the scan target area, without impeding on
the detector and within the limits of the track. Based on the angle of incidence, with the
6 degree angle on the source x-rays, it was initially calculated using simple trigonometry
that between -35 and -37 degrees should provide the best receiver angle depending
upon the detector used.
Figure 3-13. Calibration curves depicting light and dark calibrations for 1.5 degrees less than ideal, ideal, and 1.5 degrees more than ideal.
39
However, experimental testing demonstrated that the calibration curves and
image quality of the detector yielded better results if the detector was angled less than
20 degrees, more closely to zero. As shown in Figure 3-13, the light calibration
represents the signal to the detector when the x-ray tube is on, while the dark calibration
represents the signal to the detector when x-ray tube is off. A difference of 1.5 degrees
can greatly impact the difference in the light and dark calibration curves.
The first step to calibration was to use a series of lead strips 2.5 cm wide by 0.1
cm thick as a metric for image quality in a succession of test scans; for the calibration
purposes, the length of the lead was inconsequential as long as it covered the majority
of the scan area. The strips were placed on top of MPB in intervals of 2.5 cm
separation, 5 cm separation, 7.5 cm separation. By moving the scan table through the
scanning area with, even without knowing the ideal angle, each scan produced a series
of light and dark regions, which can be interpreted as the difference between the highly
scattering MPB and the low scattering lead strips as shown in Figure 3-14.
Figure 3-14. Image of the lead test strips taken through CsI:Tl detector at 6 degrees (T) and with a camera (B). Photo courtesy of T. Barker
40
Although initial calculations suggested that approximately 35 to 37 degrees was
the ideal angle for the detector, by repeating the scan at differing intervals, the results
show that ideally, the CsI:Tl detector should be at approximately 6 degrees as shown in
Figure 3-15. For the GOS detector, the process was repeated until the angle was
determined to be 27 degrees. The difference in angle between the two detectors is
most likely a result of each detector’s unique properties in terms of designs.
Figure 3-15. Angle Comparison of each angle for the CsI:Tl detector. Going from top to bottom: 0 deg, 3 deg, 6 deg, 10 deg, 15 deg, 20 deg,
The opening slit in the detector designed to allow photons to enter the detector
simultaneously collimates the impinging photons. As a result, the GOS detector’s slot
with a width of 1.25 cm allowed more energy to impinge on the pixels whereas the
CsI:Tl detector’s slot width of 0.15 cm allowed less energy to impinge on the pixels. In
addition to the pixel size differences of the GOS detector, 100 micron, and the CsI:Tl
detector, 800 micron, the slit sizes reduced resolution of the CsI:Tl detector. This
0 deg.
3 deg.
6 deg.
9 deg.
12 deg.
20 deg.
41
resulted in not only differing angles between the two detectors, but also differing power
requirements and image results, as best shown in Figure 3-16.
Figure 3-16. Ideal Angle comparison between CsI:Tl and GOS detectors; the CsI:Tl is at 6 degrees (T), while the GOS detector is at 27 degrees (B).
Upon verifying the ideal angle, alternating the power and current of the x-ray tube
yielded little change in the ideal power settings for the CsI:Tl detector; however, the
GOS detector performed better at 375 kV with 3 mA. The contrasted results of the CsI:
Tl detector power tests are shown in Figure 3-17.
Figure 3-17. Comparison of power scans from the CsI:Tl detector, using 1 lead shimmy (R) followed by a 12g copper wire (L).
150 kV
200 kV
250 kV
300 kV
350 kV
400 kV
450 kV
42
Other Tests for Image Resolution
In order to verify detector capabilities and image clarity, other scans beyond the
initial calibration angle occurred. This is in part due to image streaking across multiple
images from both detectors. Additionally, the scans verified the expected results from
scans made in previous work by authors, Dr. Kelley, Meng, and Bougeant. 1, 8-10
Although each of the scans in their previous work were conducted differently, repetition
of their work served as a litmus test for detector capabilities, scan speed, integration
speed, and resolution.
Figure 3-18. Diagram of Initial target area with lead shimmies perpendicular to the detector and parallel to the detector
43
.
Figure 3-19. Scan results from CsI:Tl detector of shimmies that are perpendicular to the detector. No lateral separation is observable in this scan.
The first reason this proved important had to do with the original angle
maximization experiments. Initially, the 2.5 cm wide lead shimmies were spaced evenly
apart in the scan area, as shown in Figure 3-18. However, the results had little contrast
between the lead shimmies, as if the whole scan area was darkened out, as shown in
Figure 3-19. These results had so little contrast, that they were worthless from an
imaging standpoint. However, upon rotating all of the shimmies 90 degrees and
spacing them as described in the experiment setup, the lead shimmies began to appear
in the scan image.
Figure 3-20. Wire spacing scan results image and corresponding photograph. Photo courtesy of Travis Barker.
44
Additionally, following the calibration using lead shimmies that were parallel to
the scanning slit, tests occurred using strands of wire as samples of different wire
gauges and compositions as shown in Figure 3-20. Specifically, 12 g solid wire, 14 g
solid wire, and 14 g stranded wire. Although the results were not ideal in image clarity,
they demonstrated some of the limitations in detector capabilities, as well as
emphasized the need for an algorithm to aid in identification. The data from each scan
is an image grey scale converted to a matrix number value. The matrices can be easily
interpreted in MATLAB using either “imageSC”, as was the case with Meng’s work in
Figure 3-21, or using contour and “contour” mapping functions.
Figure 3-21. Liesenfelt’s rasting with 1mm resolution (TL), Bougeant pencil beam image (TR) and fan beam image (BL), Meng (BR) CIBR “SXI” image1, 9,10
One of the more definitive scans performed by Dr. Kelley, Meng and Bougeant
were a series of letters to depict accuracy and maximization of their imaging techniques.
1, 9, 10 Similar scans were performed using lead shimmies in the shape of “UF” with the
45
CsI:Tl and GOS detectors. The scan speed was set to 0.85 cm/s, the same scan speed
used for pressure plate identification in later tests. Additionally, the integration time was
the same as the pressure plate experiments; the angle of maximization was set to 6
degrees as well, with the gold collimator as the standard collimator.
Figure 3-22. Scan results of UF image scan taken with CsI:Tl detector (T) and GOS detector (B).
The significant difference between the results depicted in Figure 3-22, and
results obtained by Bougeant is timing and imaging methods. Bougeant’s scanning
method used a long acquisition process in which he rotated the detector on along a
central axis, with the face still oriented towards the experiment.1 Although Bougeant
had incredibly successful imaging results for the “UF” scan experiment, his scan time
46
was on the order of three minutes.1 Comparatively, the results in Figure 3-22 took less
than 30 s and every pressure plate scan took exactly 35 s. Although Bougeant was
highly successful in his image quality, the timing alone disqualifies it as a potential
solution in a combat theater due to the security concern involved in waiting for a scan to
finish.
The streaking, seen as lines that run the length of the image, is a result of
collimator construction, external noise, and electronic noise within the detector. Given
the focus of this research is on finding the wire not identifying the type of mine, the
streaking could potentially be ignored for image resolution, as even a darkened portion
of the scan could be representative of a less scattering section of the target area, such
as a copper wire. As a result, the test results proved adequate to determine the
contrast between the metal and MPB.
Initial CBI Testing
Initial testing for the pressure plate device was focused on distinguishing the
presence of the wire in the middle of the device with an unobstructed line of site from x-
ray source to the pressure plate, ending at the collimated detector. With the absence of
any matter other than air that could obstruct the initial x-rays and impinging photons, the
initial scans would determine if the original theory of interrogation based on the wire
would hold.
The pressure plate target area was prepared according to the design depicted in
Figure 3-23. Using the same dimensions as the angle maximization system, both the
CsI:Tl detector and the GOS detector were rotated to their ideal angular position, 6
degrees and 27 degrees respectively. The lead and plywood baseplates remained on
the scanning table to serve as backstops for the x-rays and to provide a flat surface for
47
the movement of the pressure plate and two marker MPBs. The first MPB served as a
calibration board at the beginning of every scan, while the second MPB provided a
highly scattering surface at the end of the scan to denote the end of the experiment.
Figure 3-23. Diagram of the Pressure plate set up (L), with picture (R). Photo courtesy of Travis Barker.
Using this design, even with a reduced resolution, the detector data should
reflect a bright section representative of the first MPB, followed by a darker section
representative of the lead base plate. Following these characteristic shading bands, the
pressure plate would appear as a brief light section for the wood on the pressure plate
trailed closely by a dark band for the copper wire, and a final lighter section for the end
of the pressure plate. The end of the scan target area would consist of a dark band
followed by a light band, as the order of materials is inverted from the beginning section
of the scan. Even if the target area were covered by sand, a similar, albeit scaled,
contrast of light and dark bands should occur. This will be discussed further in the proof
of concept, expected results.
48
Following initial CBI scan tests with an unobstructed path between the x-ray tube,
pressure plate, and linear detector; obstructed scans were conducted. These came into
two categories, marked and unmarked scans. Marked scans were conducted using
lead shimmies on top of the sand in order to provide a marker for the pressure plate
start and finish in the event the image clarity made it difficult to discern the target area.
Unmarked scans were conducted using no markers in order to simulate field conditions.
To help balance the sand and serve as image markers, the base plate and MPB from
the non-covered scans remained in place as shown in Figure 3-24.
Figure 3-24. Diagram depicting sand scans with and without markers. Scan area same as depicted in Figure 3-23 (1), Sandbag (2), Pb Markers (3).
(1)
Scan
Direction
(2)
(3)
(1)
Scan
Direction
(2)
49
CHAPTER 4 PROOF OF CONCEPT
Expected Results
Regardless of the scanning detector used, the proof of concept images should
follow a general trend. Knowing that most high resolution CBI scans are generated
either by lengthy scanning times, or the result of multiple scans through rotating angles,
the expected resolution of pressure plate is not expected to be high.
Figure 4-1. Diagram depicting expected results with ideal scenario (T) and probable scenario (B).
As seen in Figure 4-1, the best-case scenario with imaging would be to see the
copper wire stand out among the sand, wood, and lead baseplate; however, darkening
of the wire region is also acceptable as a result. This would indicate the presence of a
material that is highly absorbing, unlike the highly scattering MPB, sand, or water.
(1)(2)(5) (3)(6)
(7)
(9) (4)(8)
(1)(2)
(3)
(5)(6) (4)
(7)
(8)(9)
(1)(2)(5) (3)(6)(9) (4)(8) (7)
50
Unobstructed Scans
Initial scans were performed using the GOS detector. This was in part due to
equipment availability, but also due to the higher resolution of the GOS detector. The
initial hypothesis was that a higher resolution with a thicker collimator might allow for
greater detail from the backscattered photons, and thus ensure the highest quality
image of the wire. Every scan used for proof of concept and results took exactly 35 s,
6% of the scanning time used by Meng.10
Figure 4-2. GOS detector calibration curves with the gold collimator (T) versus black collimator (B), with x-ray activation images. Black (R) and gold (L).
51
Originally, due to pixel size, some of the GOS detector images were performed
using the black collimator to compensate for the large number of pixels. The challenge
with this is it tended to darken the image entirely as seen in Figure 4-2. However, the
black collimator also severely dampened a lot of the signal that was needed to discern
the difference between light and dark calibrations. As a result, even with image
processing, the first scans with the GOS detector and the black collimator were hard to
read and discern an identifiable pressure plate as plainly visible in Figure 4-3.
Figure 4-3. Initial scan results with the GOS detector unobstructed using the black collimator. Pressure Plate wires highlighted by red box
Simultaneously, the testing which occurred with the CsI:Tl detector and the gold
collimator proved excellent for the initial angle maximization. As a result, the GOS
detector scans occurred using the gold collimator as well, following a repeat of the angle
maximization experiment. This produced better results, following the expected trend of
darkening and lightening bands in the scan area, representative of the changes
between highly scattering and absorbing material.
Figure 4-4. Final unobstructed image for the GOS detector using the gold collimator. Red box denotes the wire location
52
It is worth noting that the GOS detector images consistently had streaking of the
images, or lines that follow the length of the image, which can be clearly seen in Figure
4-4, regardless of which collimator was used. During the initial collection, these streaks
could not be removed; as they are most likely the result of either a bend in one of the
collimator septas, a dead pixel, electronic nose, or a combination thereof. However,
during post processing, removing the streaks could be possible.
For the CsI:Tl pressure plate, the resolution was even lower than that of the GOS
detector. As a result, initial scans seemed less detailed. This proved beneficial, as
although the resolution decreased, it seemed to lessen the general impacts of noise on
the detector. Initial calibrations between the GOS and CsI:Tl detector demonstrated
that the CsI:Tl detector was more sensitive in that there was a greater separation
between the dark calibration, when the x-rays were off, to the light calibration, when the
x-rays were on as shown in Figure 4-5. This helped the detector more clearly
discriminate the image by reducing the SNR.
Figure 4-5. GOS (B) and CsI:Tl (T) Calibration curves. Noise is similar for both, but the comparative SNR allowed the CsI:Tl detector to be more responsive.
53
Finally, the details in the wire were not captured with the black collimator or the
gold collimator by either detector. Although both detectors operated well with the gold
collimator for detection of the wire, using a series of rotating scans similar to Meng’s
work may increase the image resolution.10 It is important to note that the clarity of the
wire may not improve upon beginning unobstructed scans, as the sand and water will
continue to alter the image through lateral migration due to scatters with in the medium.
However, the banding originally mentioned in Figure 4-1, can be clearly seen in Figure
4-4; thus proving that the wire region can be identified.
Obstructed Scans
Upon establishing that both detectors could detect the copper wire when the x-
rays were unobstructed, the next challenge became to add sand to the experiment in
order to simulate a lightly buried pressure plate. In order to do this, 1 gal and 2 gal
Ziploc bags were filled with playground sand and placed on top of the pressure plate
system used for unobstructed scans. These sandbags filled to the top were
approximately 1.5 in or 3.81 cm thick. Although some Army mines are buried as deep
as 8.5 in or 22 cm, 3.8 cm is sufficient for a “lightly buried” pressure plate. A lightly
buried pressure plate is a pressure plate that is buried deep enough in the ground to
provide enough camouflage to prevent it from being easily detected with the naked eye;
however, it can be interrogated using compressed air, a mine detection rod, or other
physical means.
Given the experimental parameters, it was assumed that the effects of the bag
for the sand is negligible given it has a combined Z value less than that of the wood
used for the MPB, or the basal wood spacers. Because of the randomized nature of
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how sand falls on the target area, the bags still needed to be beaten and molded to form
the flat surface depicted in Figure 4-6.
Figure 4-6. Sand scan diagram
At times the depth or thickness of a portion of the sand would be greater in the
voids of the scan area, specifically, between the pressure plate and the MPB calibration
marking boards. This caused the darker sections of the lead baseplate to not always
appear in images the way they appeared in the unobstructed scans. For this purpose,
lead markers made from the shimmies used in the angle maximization experiments,
were placed on top of the sand in conjunction with the vacancies between the MPB and
the pressure plate, where the lead base plate would usually be seen.
Figure 4-7. Obstructed diagram and side picture with lead markers. Photo courtesy of Travis Barker.
Scan
Direction
55
Figure 4-8. Top view of the scan area. Photo courtesy of Travis Barker.
Following the experimental design of the obstructed pressure plate shown in
Figure 4-7 and Figure 4-8, the initial scans proved difficult to discern not only the exact
location of the wire, as expected, but also a shaded region that would indicate the wire’s
presence. In order to eliminate potential confounding variables, multiple sources were
identified that would affect the resolution. The detector slit on the CsI:Tl was
exceptionally small and limited a great deal of signal from reaching the scintillators. The
slit on the source collimator was potentially too wide to allow enough x-rays to reach the
experiment.
Finally, the experiment may have been too far from the source or the detector.
For simplicity sake, the distance between all three parts of the system was identified as
the first variable to manipulate, as the height of the table was easier to alter than the
collimator on the x-ray source or the slit on the detector due to mechanical limitations
with the equipment.
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In order to determine if the reason was due to a distance issue between x-ray
tube, the detector, and the experiment the height of the experiment was adjusted. The
original height of 23.1 cm was established in part due to the necessary vehicular
clearance of military vehicles. With the emergence of “V-Hull” designs, the more
effective the system is the further from the ground the better. For the initial experiment,
in order to keep signal integrity to high and reduce lateral migration, a distance of 23.1
cm was chosen as the distance from the detector and source to the lead baseplate is
slightly greater than half of the ground clearance specifications for a High Mobility Multi-
Wheeled Vehicle (HMMWV) or “Humvee”, which has a ground clearance of
approximately 40 cm depending on the variant. Conceivably, this distance could
change in future work, if the design proves useful in detection alone rather than
detection as a part of nondestructive interrogation.
Although the x-ray tube and detector could be moved, moving the height of the
table reduced the number of variables that could affect experimental outcome. Upon
doing so, new angle maximization experiments were conducted. These experiments
led to two new ideal maximization experiments with an ideal angle for the GOS detector
of 33 degrees, while the new maximized angle for the CsI:Tl detector became 13
degrees.
Surprisingly, the CsI:Tl detector also demonstrated that in addition to 13 degrees,
16 degrees may also prove to be a maximized angle. Although for the remainder of the
experiments, 13 deg was the primary angle used, some experiments were conducted
using the 16 deg mark as well for later analysis. The final maximization results can be
seen in Figure 4-9 and Figure 4-10, with the CsI:Tl detector at a 13 deg angle. The
57
results of which are detailed enough to identify some shading as a result of copper wire
samples.
Figure 4-9. New Angle Maximization for GOS detector at 3.75 cm. Image shows Lead strip (1), 12g Solid Copper Wire (2), and 14g Solid Copper Wire (3).
Figure 4-10. New angle maximization for the CsI:Tl detector when raised 3.75 cm.
Given the new maximized angle for the elevated target area, initial scans once
again showed little detail in terms of where the wire should be. At this point, it was
theorized that because the detector is designed to operate like a camera, the positional
focus needed to change. A camera is often focused at the appropriate distance based
on the focal point the photographer wants to capture. If photographing a person against
scenery, the photographer would focus the camera on the persons face, not the
mountains behind them. Conversely, if the photographer wants to focus on the
mountains, focusing on a closer subject would blur the scenery. This theory was tested
(1)(2)(3)
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first by conducting an experimental scan with the raised table location shown in Figure
4-11, with no markers present.
Figure 4-11. Raised 3.75 cm scan image of obstructed scan taken using the CsI:Tl detector. Note no discernable bands or shapes in the scan.
Theoretically, the detector should work in a similar manner as it is designed to
have similar characteristics to a camera. Once the focal point, or maximized angle of
the lead strips when raised 3.75 cm was determined, the entire target area was lowered
back to the original height. Even after calibrating the detector to the sand on top of the
MPB, the change in focal point allowed the copper wire internal to the pressure plate
obstructed by the sand, to be the primary focus of the detector in each experiment. As
a result, the first scans with the GOS detector were successful in identifying the some of
the banding that was first theorized before experimentation began.
Figure 4-12. GOS scan at original height with two lead markers (1) to identify the area of interest for the pressure plate's wire (2).
(1)
(2)
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The results in Figure 4-12 proved successful with the GOS scans initially, where
the most darkened regions in the middle of the scan represented the lead markers
originally placed in the scan region to denote the space between the MPB and the
pressure plate. Between these markers, the image brightens slightly to represent the
wood from the edge of the pressure plate.
Figure 4-13. Final sand obstructed scan with markers using the GOS detector. Pressure Plate located within the red rectangle.
In the center of the image shown in Figure 4-13, the darkened region represents
the wire itself. Once again, streaking did present an issue with the GOS detector that
was not as predominate in the CsI:Tl detector. Another distinct difference between the
GOS and CsI:Tl detector was the appearance of the lead markers. As shown in Figure
4-13, the lead markers do not take up the entire scan window. Contrasting this with
Figure 4-14, the lead markers take up the majority of the scan window during that
portion of the scan.
Figure 4-14. Final sand obstructed scan with markers using the CsI:Tl detector. The red box denotes the wires. Adjacent darkened bands represent the lead markers.
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The final obstructed scan that was briefly focused on was that of water
obstructions. Standing water, such as in a wadi or an irrigation canal, can sometimes
mask the signal of GPR. The IEDs in these regions can still be detected, but it requires
a little more effort on the part of the combat engineers. As a result, one of the final
scans conducted was using water as the obstructing medium as seen in Figure 4-15.
Figure 4-15. Water bag obstruction experimental design. Side view (L) and Front View (R). Photo courtesy of Travis Barker.
A 2 gal water bag was filled, resulting in a 3 in or 7.62 cm thick obstruction.
Unlike previous material scans, the angle was not maximized. This was to simulate
whether a detector that was configured for a dry sandy environment could detect a
pressure plate hidden in a patch of road that was filled with water, like a pothole. The
results of which are depicted in Figure 4-16.
Figure 4-16. Water obstructed scan results with the CsI:Tl detector. The Pb Baseplate region is brighter than the MPB region, contrary to the sand obstructed scans.
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Having completed the last of the obstructed scans, the next challenge was
analysis. The contrast that reveals the wire is not readily visible, requiring hours of
familiarity. Algorithms must be leveraged to improve contrast and readability. Finally,
the easier something is for a soldier to use, the more likely they will actually use it. To
that end, improving resolution became a focus of the results and analysis for the
remainder of the research.
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CHAPTER 5 RESULTS AND ANALYSIS
Grey Scale Observations
The data from the proof of concept images show changes in the grey scale as
the scan moves along. Although the scans are acquired through a vertical movement of
the image, the shading patterns correspond to the lightening and darkening contrast
that is expected as the scanned region transitions between the higher and lower
scattering materials as seen in Figure 5-1.
Figure 5-1. Scan results of a lead shimmy. The MPB maintains a consistent brightness whereas the lead shimmy gets darker towards the center.
These images are not of such fine detail as to depict exact border regions
between materials in the target area. As a result, there is some shading that casts
doubt on the specific locations of the pressure plate and the wire. These details could
be refined if the experiment used a longer acquisition time or a multi-angular approach
that rotated the scan target in order to provide a composite image. However, increasing
the time of the scan contrasts with the end objective, which is proof that CBI has the
potential to be a non-destructive interrogation and detection method when combatting
IEDs.
Consequently, clearly discriminating the regions of the scan data with post
processing could provide clarity to the point of providing an image that can emphasize
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the copper wire. The first method used in trying to do this was simple brightness and
contrast adjustments with a photo editor. Although the regions became sharper or
dulled depending upon the adjustments made, none of the results provided a more
definitive solution to defining the pressure plate in the scan area as shown in Figure 5-2.
Figure 5-2. From T-B: GOS detector unobstructed scan, with brightness 100%, contrast 100%, contrast increased and brightness decreased.
Grey Scale Analysis
The next step to proving the changes in contrast are in fact the material
obstructed by the sand occurs using a scientific post-processing program like ImageJ or
Gimp. These programs allow for a grey scale analysis across the entirety of the image.
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This analysis did not remove the noise created by the detector as can be seen in Figure
5-3, but contrasting peaks were generated at intervals consistent with the expected
materials underneath the sand.
Figure 5-3. Grey scale graph of a unobstructed CsI:Tl scan. The decrease in grey value at pixel 950 and 1250 represent the copper wire in the pressure plate.
Using this image method, it was possible to demonstrate differences in the
detected image density and resulting scatters. It is important to note that the image in
Figure 5-3 has multiple spikes even within small section pixels as a result of noise and
the scintillator behavior. The general trends of the grey scale analysis serves as the
transitions between materials. High peaks correlate to brighter points in the image, or
highly scattering material; while lower peaks indicate darker points in the image, a result
of more absorbing materials.
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Figure 5-4. Grey Scale image aligned with the corresponding image of the pressure plate obstructed by sand. Image taken using the GOS detector.
Consistently, images taken with the lead markers on top of the sand obstruction
show a large drop, indicating the marker location as shown in Figure 5-4. This is
followed by a rise for the wood from the edge of the pressure plate, trailed by another
dip resulting from the copper wire.
Using the grey scale analysis, these dips and rises corresponds to the following
values depicted in Table 5-1. The results correspond to Figure 5-5, the grey scale
analysis of a CsI:Tl scan image. Although the tail of the image drops more than the
initial drop from the lead marker when comparing pixel locations 250 and 1500, this is
most likely a result of a change in scatters as the scan table exits the experimental
window. Regardless, Table 5-1 and Figure 5-5 demonstrate consistencies with respect
to the material in the scan window and relative scatters.
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Figure 5-5. Grey Scale image aligned with the corresponding image of the pressure plate obstructed by sand. Image taken using the CsI:Tl detector.
Table 5-1. Median values of the grey scale analysis at each dip and peak for the CsI:Tl detector.
Notable Point Approximate Grey Scale Value Corresponding Material
1 62,000 Wood
2 55,000 Lead Marker
3 64,000 Wood
4 60,000 Copper Wire
5 61,500 Wood
6 55,000 Lead Marker
Accepting that each of these values depicts a shift between the material exposed
to the fan-beam x-ray at a specific location with respect to time during the scan
transforms the understanding of the data from an image quality analysis to a
mathematical data analysis. In other words, every scan will be a set number of pixels
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wide depending upon the detector’s number of pixels, while the length of each scan is a
function of time. By using ImageJ to transform the image into an array of data points
with the x axis equal to the number of pixels in the detector and the y axis equal to scan
position at a point in time, it is possible to develop an algorithm in order to analyze the
image and attempt to clean it further than what adjusting brightness and darkness
contrast alone could do.
Algorithm Conception
In order to remove the sudden spikes in data depicted by the detector, all of the
pixel columns were averaged in sets of ten to find average pixel brightness. This was
first performed using MS Excel’s heat map capability. All of the data points were
averaged in a separate worksheet from the original data. These average values were
then given a conditional coloring system automatically by MS Excel. This allowed for
those not familiar with the research to start to see trends in the brightness not easily
depicted by the grey scale images alone. An example of this contrast is clearly depicted
on Figure 5-6. Furthermore, this technique started to adjust for the false data points
that were the result of any number of electronic factors unrelated to the actual photon
striking the pixel.
Figure 5-6. MS Excel heat mapping using conditional formatting, demonstrates the transition between the lead spacer and MPB.
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Although MS Excel has many easy to use functions for data analysis, in order to
develop a more user-friendly algorithm and resulting image, the data was moved to
MATLAB for more processing. After removing the noise through the averaging of data
points, the next challenge would be to help establish known quantities.
Everything in the target area that was scanned either consisted of MPB, sand,
copper wire, or the lead base plate. As a result, each of those substances has a known
material composition. MPB for example is known to have approximately 50% carbon,
42% oxygen, and 6% hydrogen; while playground sand, silicone dioxide, has only
silicone and oxygen. The copper wire and the lead baseplate each have their own
element numbers based on their composition. The Z number for lead (Z=82) is
significantly larger than that of carbon (Z=6), hydrogen (Z=1), silicone (Z=14), or oxygen
(Z=8). Copper (Z=29) has twice the Z number of silicone, but only a quarter the weight
of lead. These differences aid in the discrimination of the material weight, or in this
case, the amount of scatters they will produce.
Previous land mine research focused on finding the air pockets within the mines
as a unique signature to look for in the ground because the air is mostly nitrogen (Z=7),
as is the plastic explosive, while dirt has a large composition of silicone, nitrites and
carbon from organic matter. This led to challenges in using CBI in anything more than a
pencil beam configuration as it was difficult discern the difference between normal
matter and land mines themselves. This is still a challenge for low metal content land
mines even when using the proof of concept this research demonstrates. However,
when detecting a pressure plate with a known copper signature, defining the known
contrast of the wire’s signature, a wood signature, and a lead signature would allow for
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a histogram approach to the scans. In other words, carefully cleaning the data and
comparing it to known values allows for the identification of the material based on the
statistical probability that it is one of those known materials.
Algorithm Noise Reduction
With that in mind, the following algorithm was developed. First, all of the data
points from a scan were averaged in groups of ten in order to help stabilize the data into
a cleaner group of results as discussed with the MS Excel heat map. As shown in
Figure 5-7, doing this will establish a median value that serves as a more accurate
representation of the actual brightness; thereby removing the false data that results
from integration time and electronic noise.
Figure 5-7. The original data for first 30 data points on pixel 96 on the CsI:Tl detector on a obstructed scan (Black) averaged to produce a flatter series of data (Gold)
Even with processing the data and stabilizing the peaks as shown in Figure 5-7,
the SNR was still higher than it should be. Simply looking at the calibration curve during
the maximization experiments demonstrated this limiting variable. An ideal situation
between the dark and light calibration would yield a dark calibration at near zero, while
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the light calibration would remain at its current curve. However, the limited spacing
between the light and dark calibration as shown in Figure 5-8, demonstrates that the
noise is still too high in the detector.
Figure 5-8. Calibration curve for the CsI:Tl detector during angle maximization at 6 degrees
In order to remove the noise, a standard noise reduction technique needed to be
established. This was performed by first conducting an experimental scan of 30
seconds without the table moving or the x-ray source on after initial calibration. This
data still demonstrated that there was a consistent noise in the background as
presented in Figure 5-9.
Figure 5-9. Background noise. Despite the x-ray source not emitting x-rays, the detector continued to show bright spots, representing photon signals.
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By obtaining this data over 30 seconds, the array of data collected by each pixel
over time could be averaged to generate an average noise by pixel. Each pixel had a
different amount of SNR as each scintillator acts independently of the adjacent
scintillators. Therefore the noise array had to be generated and subtracted by pixel in
order to account for the interference that each scintillator’s unique properties would
generate. This array was then subtracted from the averaged image data of the
pressure plate in order to remove the noise entirely from the scan. With the noise
removed using the average noise by pixel, both the light and dark values were
decreased by the same increment. Although this had a little affect proportionally
between the dark and light values, it still established that the data for each scan was
closer to the true signal, unimpeded by the SNR.
Known Variable Definitions
Figure 5-10. Construction of the copper wire sample. Photo courtesy Travis Barker.
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With the noise accounted for, establishing known variables became the next step
in developing a histogram approach to identifying the material components of each
scan. To do this, samples of the variables had to be fabricated for identification under
sand if scanned. The copper sample developed in Figure 5-10 was created by simply
using strips of wire under a sheet of solid pine, the sample would mimic the copper wire
in the pressure plate. The sample was then placed face down like the lead and MPB
samples. Following this, the materials shown in Figure 5-11 were scanned while
covered in sand in order to provide a known region of data with the corresponding
brightness relative to that materials’ potential to scatter x-rays.
Figure 5-11. Known material samples prior to and after being covered by sand bag. Photo courtesy of Travis Barker.
The data from these scans was then processed to select the ideal data range. In
order to prevent transition areas from clouding the algorithm, the data points that were
selected maintained as solid a coloring as possible in order to prevent shading from
altering the expected values for the bin definition. These regions were then converted
into an array to be averaged by pixel as known material data. As a result, each pixel
has its own unique value for each known material. Like the obstructed scans, noise
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was subtracted from the known wood, lead, and copper values in order to ensure that
even the known variables were as close to their true values as possible.
Initial Histogram Development
With known variables defined, the next step was to begin classifying each data
point into bins. For each pixel over time, each data point represents a specific location
in the scan target area. After averaging out the anomalous spikes and removing the
noise, the relative brightness of each location is closer to either the relative brightness
of the copper, MPB, or lead covered in sand. By subtracting the cleaned data, identified
as the averaged data with noise removed, from each of the known cleaned variables,
the closest absolute remainder to zero is most likely the identifying material. As a
result, that data point in the array can be classified as a 1, 2, or 3 if it is wood, lead, or
copper respectively. An example of this is demonstrated in Table 5-2.
Table 5-2. Example break down of data points forced into the three original histogram bins, the “Forced Algorithm”
Data Point Value Copper Value Lead Value Wood Value Histogram Bin
1 54,750 60,000 55,000 61,500 2
2 59,850 60,000 55,000 61,500 3
3 62,000 60,000 55,000 61,500 1
4 59,400 60,000 55,000 61,500 3
However, this method of histogram fails to fully integrate statistical deviation into
the equation. As a result, a fourth category was developed. Rather than stating that the
closest value to zero must be that material, the standard deviation of each known
variable’s average value was included to account for the transition, or shaded regions.
Even if a data point were close to zero, but outside the standard deviation range, it
would be thrown into the unknown category. This ensures that rather than forcing the
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data to work according to the algorithm, it accounts for potential deviations that the
system will encounter if put into real world use. The bins stayed the same with the
exception of adding a 0 for unknowns as depicted in Table 5-3.
Table 5-3. Example break down of data points using the final histogram bins, the “Quaternary Algorithm” using a set standard deviation of 500
Data Point Value Copper Value Lead Value Wood Value Histogram Bin
1 54,750 60,000 ± 500 55,000 ± 500 61,500 ± 500 2
2 59,850 60,000 ± 500 55,000 ± 500 61,500 ± 500 3
3 62,000 60,000 ± 500 55,000 ± 500 61,500 ± 500 1
4 59,400 60,000 ± 500 55,000 ± 500 61,500 ± 500 0
Processed Results of the Algorithm
The development of the processing algorithm through MATLAB allowed for more
detailed results than previously expected. When the original data was collected, the
banding of grey scale portions of the image allowed for the identification of the most
probable locations of the wire, but it lacked resolution on the actual wire itself. The
theory behind the algorithm was to solidify these bands at a minimum, with the hope of
identifying the wire more easily. For this analysis, it is important to first analyze the
original transmission experiment as to the perceived detail of the wire, shown in Figure
5-12.
Figure 5-12. Original transmission scan collected with the GOS detector without being covered in sand
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As shown in Figure 5-12, the wires within the pressure plate are not symmetric,
but are designed to have the highest potential of interaction should the plate compress.
For that reason, one of the wires is woven in a sinusoidal manner, while the second wire
is straight. Even if the pressure plate is not compressed in a perfectly vertical manner,
the wires will connect to complete the circuit.
Figure 5-13. Final obstructed scan using markers with the GOS detector (Top) and the CsI:Tl detector (Bottom).
In Figure 5-13, the areas surrounded in by the red markers is the area of the
scans that were used for processing and data analysis. By extracting these portions,
the algorithm could focus on the target area of interest for faster processing and a
cleaner comparison between the image and the data results for the histogram
development.
Upon using the original histogram results where the data is forced into three
concise bins, the data is compiled into a contour plot using MATLAB. This results in a
clearly defined series of lines akin to the shape of the original copper wire. In the
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results represented in Figure 5-14, the yellow with a bin value of 3 represents the
copper wire, while the green with a bin value of 2 represents the lead, and finally the
blue with a bin value of 1 represents the wood.
Figure 5-14. CsI:Tl obstructed scan with markers results depicted with a contour plots using the "forced" algorithm.
Although Figure 5-14 shows some promise in terms of defining the copper
signature from 750 to 1400 the forced algorithm still lacks statistical certainty. For
example, we know that the copper signature at any given pixel should depict
approximately 3 separated copper signatures at its best, and a general copper trend at
a minimum. However, between the copper signatures, a wood signature or unknown
signature should occur as a result of shading effects from the scatter blocked by the
copper signature. The opportunity to reanalyze the image in the hopes of providing a
cleaner image, further necessitates the need for the quaternary algorithm. Using the
same regions as depicted in Figure 5-14, the quaternary algorithm provided positive
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results as well. The colors remained the same with the exception of “unknowns”
categorized in the 0 bin.
Figure 5-15. GOS (L) and CsI:Tl (R) contour plots using the "quaternary" algorithm with
a margin of error of ±𝟎. 𝟓𝟎𝝈.
Unfortunately, by adding a fourth bin, the unknown numbers increase more so
than the lead or wood regions. This can be affected in a number of ways, primarily by
increasing or decreasing the statistical uncertainty resulting from the use of the standard
deviation. In Figure 5-15, the standard deviation was set to±0.50𝜎. However, when
using ±𝜎 for the CsI:Tl detector as shown in Figure 5-16, the number of unknowns
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decreases from 271,447 to 136,688, a decrease of 50%. However, the copper signature
increases by 33%.
Figure 5-16. GOS (T) and CsI:Tl (B) contour plots using the "quaternary" algorithm with
a margin of error of ±𝝈.
Although having a high number of unknowns would seem to be problematic, it
actually has little effect on the end state of the algorithm. A high false positive rate will
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decrease the users’ trust of the system, thereby generating a potential human error
when the user ignores a true positive. A false negative rate should be as low as
possible, as it could be fatal to the end user. Currently, the false negative rate seems
negligible. The better questions are how accurate is the true positive rate, false positive
rate, and what causes the high number of unknowns?
True and False Positives
In order to determine the amount of true positives, two analysis methods were
used. The first method is a two dimensional representation of the three dimensional
area using MATLAB. As shown in Figure 5-17, the number of unknowns is
exceptionally high, with the lead and MPB signatures significantly outweighed relative to
the volume of the scan target occupied by both materials. The copper wire on the other
hand offers very little indication of its presence.
Figure 5-17. 2D side view of the target area after processing CsI:Tl obstructed scan results with markers. The scanning direction runs along the X-axis.
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The low presence of copper in the 2D view helps assert an accurate true positive
rate. In the target area for a 12 in or 30 cm scan, the 12g copper wire is only 0.15 cm
by 23 cm. That means that in any given scan, the copper should have the lowest
signature rate of any of the three materials, and including shading, of the four histogram
bins. When run through the processing algorithm,
Although this is depicted by the 2D representation in Figure 5-17, in terms of
volume of the scanned area, the copper wire is less than 0.01% of the entire volume.
This is derived by assuming that the scan area is rectangular in shape, accounting for
the height of the sand resting on the MPB (8.89 cm) which is equal to the height of the
sand on the pressure plate, on top of the base plate (0.635 cm), and 30 cm wide, with a
scan length of 30 cm. Given that number of copper signatures in the final histogram is
13% of the entire original image, the false positive rate seems high despite indications
from the Figure 5-17 that it is not.
However, when viewed from a 2-D perspective, the entire wire consumes
approximately 5 cm by 22.5 cm given the sinusoidal pattern of the bent wire and the
shading results in the image. These dimensions assume that the area of the copper
wire occupies is a rectangular approximation. From that perspective, the area of the
wire consumes approximately 12.5% of the 900 cm2 scan area.
Therefore, from a 2-D perspective, Figure 5-17 shows that the histogram method
of discrimination between copper, wood, lead, and unknowns is very accurate given the
0.5% difference between reality and copper histogram counts. This false positive
versus true positive metric of reality versus counts, proves even more useful with later
analysis methods.
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Figure 5-18. MPB False Positive Test experimental setup. Photograph courtesy of Travis Barker.
In order to more accurately determine if the false positive rate is a result of the
algorithm or a result of residual shading in the scan, the algorithm was tested using a
sandbag over the top of MPB to conduct a false positive test as demonstrated in Figure
5-18. This was performed as a way of ensuring no false positives. As shown in Figure
5-19, the scan itself showed no contrast in the experimental range. Furthermore, when
processed through the quaternary algorithm, almost none of the averaged pixel
locations equated to a copper signature.
Using the metric to the false positive test comparing the signatures of copper in
the histogram results to the reality, the copper signatures should be at 0%. Over the
total area, the number of copper data points totaled to 74 out of 428,160 data points, or
a 0.017% copper signature. As a result, it is clear that the false positive rate is
incredibly low. Therefore, not only does the histogram method provide a clear definition
of copper if there is a copper wire present, but it give almost no false identification in the
event the scan area is absent of a copper signature.
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Figure 5-19. False positive scan image data (T) and processed results (B).
Part of challenge with the unknowns is that even with processing, the image will
not have a perfect resolution. Lateral migration of the photons will cause a contrast shift
for the detector. Like a camera, changing the focal point and the lighting will sometimes
cast shadows for the detector. As a result, some of the contrasting that is occurring in
the image is an effect from the pressure plate; other contrasting may be a result of the
lead markers themselves.
In the case of the false positive test, the histogram produced 0.017% copper,
with 99% of the signatures reading as unknowns. Given that the copper signature is so
low, the false positive rate for this detection method is reasonably accurate. The
shading effects that are evident the marker scan in Figure 5-19 are simply a result of the
copper presence, and not an error in the algorithm. However, a better variable
Scan
Direction
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definition for the copper, lead, and wood signatures would help improve the percentage
of unknown data points. These results are summarized in Table 5-4.
Table 5-4 Consolidated Results of false positive and false negative comparison Test Cu Area
(cm2) Total Area (cm2)
Expected Cu%
Histogram Cu Counts
Total Counts
Histogram Cu%
±0.5𝜎 112.5 900 12.5% 48947 376,128 13%
±𝜎 112.5 900 12.5% 145864 414,336 35%
False Positive
0 900 0% 74 428,160 0.01%
As a result, more tests were conducted without the use of lead markers in order
to prove that the change in contrasting was not a result of the lead markers casting a
shadow.
Obstructed Unmarked Tests
In order to simulate ideal field conditions as well as remove the possibility of
shading effects as a result of the lead markers, were conducted obstructed unmarked
tests. These tests were entirely the same setup as the original obstructed tests; with
the exception of removing the markers from the scan target area.
Figure 5-20. Obstructed scans without the lead markers both from the GOS detector (T)
and the CsI:Tl detector (B).
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The results of these images depicted in Figure 5-20 made it harder to discern the
wire location despite previous experience reading the images on a routine basis. This
proved the necessity behind using the algorithm for image processing as “eye-balling”
the copper region proved difficult, even with the grey scale analysis. What’s worth
noting is that despite the removal of the lead markers, the image still presents
indications of the shape of the wire. This is evident by the trend of yellow data points
between line number 500-1000 in Figure 5-21, which correlates to the wave pattern of
the compression wire that was originally shaped to ensure contact between the two
ends of the circuit. However, there is still a reasonably high copper signature as per
Table 5-5.
Table 5-5 Consolidated Results Contrasting Copper Histogram Results Test Cu Area
(cm2) Total Area (cm2)
Expected Cu %
Histogram Cu Counts
Total Counts
Histogram Cu %
±0.5𝜎 - Marker
112.5 900 12.5% 48,947 376,128 13%
±0.5𝜎 – No Marker
112.5 900 12.5% 145,864 414,336 35%
Figure 5-21. Processed Results of the CsI:Tl detector Obstructed Unmarked Scan.
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There is some spotting of the copper signature above that concentrated path,
and a larger dip on below that with indications of wood and unknowns. The unknowns
are most likely a result of the shading effects from the wire, but more importantly, the
wood definition surrounding the copper lends itself towards the detector discriminating
between the wood and the wire within the pressure plate. In other words, the algorithm
has helped the detector compensate for resolution proving that it cannot only help
identify the presence of a copper wire signature, but potentially identify it’s shape.
The added benefit of this is it demonstrates that a fan beam has the potential to
be equally beneficial to detection and identification given the composition of a low metal
content mine. At the time that this algorithm was developed, Dr. Michael Liesenfelt
work provided a solution to dead and dark corrections using an analysis method he
used to increase the resolution of his imaging software.9
Liesenfelt’s Cumulative Distribution Function and Streak Correction
Dead and dark pixels contribute to the noise in the detector because of either
electronic noise, a bent septa, broken or a combination there of. To correct this,
Liesenfelt utilized a moving average to create a cumulative distribution function, by
using the 95th percentile of the normalized distribution function as it corresponds to the
brightness of the image.9 From there, he divided the average 95% of the entire detector
average by the detector average for each individual pixel. The pixel value at any time, t,
should be less than 95% of the average of the entire image. If the value is not within
that 95%, the algorithm will disregard it and substitute that value with an adjacent value
that is within tolerance. The adjustment of these values also accounted for dead and
dark pixels; pixels that may not operate as they are supposed to for electronic reasons,
or affected by a septa in the collimator that is either bent or broken. By applying
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Liesenfelt’s method to my own statistical algorithm, to include the scan and the known
variables of wood, copper, and lead, the results in Figure 5-22.9
Figure 5-22. Processed image results of a CsI:Tl obstructed scan with no markers following the Liesenfelt method.9
The initial results of the change to the statistical algorithm provided even more
clarity than the original statistical method. In Figure 5-22, the brightness beginning at
position 500, and darkens again at position 1100, before lightening again at 1500
corresponds to the contrasting effects of wood from the pressure plate with the wire
located at approximately 1100. The shading within the copper regions appears to be
more defined, thereby demonstrating that the processed images not only have a copper
signature, but also the shape of the wire producing that signature. Although it is not
nearly as clear as the examples from previous work depicted in Figure 3-21, it is
promising in terms of rapid, non-destructive interrogation.
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Figure 5-23. Processed results of a GOS obstructed scan with no markers following the Liesenfelt method. True Image data (T) and Histogram Results (B).9
Similar results occurred with the GOS detector. As shown in the true image data,
the top image in Figure 5-23, the wood signature from 1000-2000 corresponds to a
wood signature from 1000-2000 on the histogram. Some copper signatures appear in
that region as well, with some lead too. These inconsistencies could be a result of
improper variable definition during the variable test scans; however, the trends in both
the true image and the histogram data demonstrate that Liesenfelt’s method
significantly helps improve the resolution of the pressure plate signature.
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Figure 5-24. Perpendicular pressure plate obstructed scan setup diagram.
As a final test of the algorithm, I rotated the pressure plate 90 degrees as
demonstrated in Figure 5-24. During initial experimentation, I struggled to get a solid
resolution between the lead strips when they were scanned lengthwise perpendicular to
the detector. Therefore, I scanned the pressure plate just as I scanned the lead strips,
lengthwise parallel to the detector. By rotating the whole setup to a perpendicular set
up, I was able to scan the image shown in Figure 5-25.
Figure 5-25. Final Obstructed Scan with no markers using the CsI:Tl detector, the pressure plate is perpendicular to the direction of the scan.
Upon processing the image with the final statistical algorithm using the streak
correction, I hoped to have a better signature for the copper since the algorithm
compensated for the lack of resolution.
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Figure 5-26. Processed results of obstructed scan with the pressure plate oriented perpendicular to the direction of the scan.
As shown in Figure 5-26, the resolution compensation of the statistical algorithm
and streak correction provides a definitive answer that CBI used in a push broom
system can clearly detect pressure plate IEDs no matter what scan direction the
detector moves across the scan target area. Given the sinusoidal nature of the crossing
wire in the pressure plate design, the series of data clusters at lines 200, 400, 600, 900,
and 1100 would seem to indicate resolution of the wire.
It is worth noting that only one of these crossing data series is gold, the
histogram color used to indicate a copper signature. This is most likely due to a
variable definition problem for the histogram average data. In order to be characterized
into a specific histogram bin, each data point must be within one half of a standard
deviation of the average pixel brightness value for that material. If the bounds of a
material were improperly identified due to shading, the material could have a poor
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comparison point for the algorithm’s characterization. Regardless of the error, the
rhythmic pattern, of the brightened portions of the processed results demonstrate that
the algorithm and the scan data have the potential to identify the IED and its internal
components effectively.
Water Obstruction
As shown in Figure 4-15, water obstruction has a significant impact on detection
definition, even though the copper signature is the only darkened portion of the original
scan’s data. Even without defining the variable values for copper, MPB, and lead when
located under water, it was possible to run a simulation to determine the resolution of a
pressure plate under 7.62 cm of water shown in Figure 5-27 between the 450-600 scan
points.
Figure 5-27. Processed image of obstructed pressure plate under 3 in of water taken with the CsI:Tl.
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Water does alter the amount photons scattered compared to the sand. However,
the resolution when covered in a water bag is still improved upon using the algorithm.
Variable definition for water was almost impossible due to the fluid nature of the water
bag on top of the known variables. This was also evident in the pressure plate scan as
the lead base plate sections were brighter than the pressure plate and the MPB
sections due to the scattering nature of water and the way it filled the voids above the
baseplate.
However, when looking at the processed results as shown in Figure 5-27, the
brightness of the lead surrounds a darkened region in the vicinity of scan position 500,
which indicates the voids of the base plate almost inversely to the original obstructed
scans with lead markers. As a result, it is very clear that the pressure plate can be
detected using a fan beam geometry through water; a medium which usually masks the
signals of GPR.
Narrowed Source Collimator
The original scans that took place all utilized a collimator that was approximately
0.95 cm in width by 10 cm in length. In order to test if condensing the source
collimation would increase resolution despite the decrease in the number of photons, a
final unobstructed unmarked test was conducted using a collimation size of 0.47 cm
with no markers. Even without using the histogram data, the image brightening and
processing in part through the Liesenfelt method provided enough clarity, to know the
darkened band vicinity of 900 in Figure 5-28 is actually the wire of the pressure plate.
The significance of this is it shows that future tests with a more confined beam would be
ideal; however, if the source collimator in a fielded prototype were damaged, the initial
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results based on a larger collimator would be effective until maintenance became
possible.
Figure 5-28. True image data from the Narrowed Collimator source. The wire is located between 800-1000.
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CHAPTER 6 FUTURE WORK
Although the fan beam push-broom scanning method proves useful for land mine
detection during interrogation, it currently falls short of a replacement method for
Ground Penetrating Radar. As a result, more work is needed to compensate for these
shortcomings. Specifically, the integration time versus the table movement time is a
synchronization issue.
In order to ensure that the table moves at the same speed as the integration time
so that each pixel integration time is near simultaneous with the movement of the target
area, further calibration is needed. During testing, decreasing the integration time with
the current detector resulted in an inability to discriminate between light and dark
calibrations. As a result, filtering noise from the scans became impossible with the
naked eye. Considering that the proof of concept scans necessitated a statistical
algorithm to ensure identification for the outside viewer, the amount of processing for
images at that faster integration speed proved unfeasible for the scope of this research.
Additionally, there is a coordination challenge that correlates to the speed of the
scan table. During one of the maximization experiments, the scan table moved too
quickly for an accurate scan, as the speed was set to a total scan time of 5 seconds. As
a result, the 1 in spacers used in previous maximization experiments produced the
image depicted in Figure 6-1. The scaling effects of the integration time, coupled with
the speed of the table, made the 2.5 cm lead strips appear smaller than normal. Had
the same integration time been set to a faster speed, perhaps the strips would have
looked normal even with the rapid table movement.
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Figure 6-1. The 2.5 cm shimmies when imaged at a faster table speed with the same integration time as previous scans.
Resolution became a secondary challenge for all the scans. Even though the
algorithm proved my observations to be accurate statistically, employing CBI as an
alternative to GPR requires additional work. Future work would need to demonstrate
improvement on not only early detection and verification during interrogation, but
identification during the interrogation phase. The primary motivation for this research
was tied to the most current fight our soldiers, sailors, airmen, and marines face; not
necessarily their future battles. Just as previous research was focused on mines only,
IEDs were the primary focus for this research. Plastic mines and IEDs will continue to
evolve to avoid detection and although some methods stay the same, such as Vietnam
era traps still used on the battle fields today, our detection and identification systems
must do the same.
In order to meet both areas of improvement, research should move towards field
testing, prototype development, and refining the algorithm processing to real time
processing. The real time processing should be relatively feasible using a series of
processors once integration time is synchronized with movement speed. However,
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utilizing the data in real time to refine an image of potential pressure plates would
require a significant amount of processing given the time it takes to analyze the current
scan data using MATLAB. Furthermore, although a height of 24 cm was used for the
detector height, increasing the distance between the detector, source, and target area
would need to occur to match the clearance of military vehicles, as a HMMWV has 40
cm of clearance.
Additionally, utilizing a series of detector and source arrays could potentially
allow for multiple depths of analysis and detection. With the push-broom method
developed by Dr. Kelley, one detector was enough to do the imaging necessary to
detect large-scale vacancies in railroad ties.8 For a smaller metallic signature, a second
detector may allow for the detection of pressure plates at faster speeds than GPR is
currently capable of. Meng’s work demonstrated the utility of combining data from a
series of scanning detectors in order to improve on CBI.10 The CsI:Tl detector found two
possible maximized angles of 13 deg and 16 deg with the original source collimator slit,
and a similar problem with 2 deg and 4 deg. on the smaller source collimator slit.
Considering these discrepancies, the potential for using two detectors with the same
source is plausible with fan beam geometry and may even produce a higher resolution
of the target area. It might also afford the detector greater depth penetration. Given the
necessity of minutes on the battlefield being put to good use, every step towards faster
detection allows commanders and soldiers to make the best decisions possible to save
lives.
Finally, although this research proved that CBI imaging under water is possible,
initial testing showed water to appear more scattering than sand. This may be in part
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due to the depth of the water was twice that of the sand, and thus even more highly
scattering in the vacancies between the calibration boards and the pressure plate.
However, in order to be able to use CBI in a plethora of battlefields, further testing is
needed to determine if these observations are medium based or a result of the relative
distance between the parts of the system. Doing so could turn CBI into a more versatile
solution than GPR, thus allowing engineering units the ability to clear amphibious
obstacles, or simply making water crossings safer.
Ultimately, more work needs to be done to allow CBI to be used in combat
scenarios. This research shows that CBI could be a viable solution to interrogation of a
suspected IED in 35 s or less. However, with computer advancements, x-ray tube
mobility, and scintillator versatility, CBI could prove to be the next great advancement in
land mine detection. Even though the government has considered it a possibility since
the 1970s, with the right combination of detectors and sources, CBI finally has the
potential to be an operational success after almost 40 years since its conception.
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LIST OF REFERENCES
1. Bougeant, O. (2009). Alternative Techniques of Backscatter Radiography: Snapshot Aperature Backscatter Radiography and Collimated Segmented Detector Scatter X-ray Imaging. Gainesville: University of Florida.
2. COMET Technologies. (2016, December 6). EVO 300DS: Small Focal Spot.
Retrieved from Portable X-Ray Systems: http://www.comet-xray.com/Products/Portable-X-Ray-Systems/PXS-EVO-Directional
3. CPT Crone, R. (2016, March 15). Sapper Feedback on Experience with IED Detection. (T. R. CPT Barker, Interviewer)
4. Furuta, K., & Ishikawa, J. (Eds.). (2009). Anti-Personnel Land Mine Detection for
Humanitarian Demining: The Current Situation and Future Direction for Japanese Research and Development. London: Springer.
5. Harding, G. (2004). X-ray scatter tomography for explosive detection. Radiation
Physics and Chemistry, 869-881. 6. Hussein, E., & Waller, E. (2000). Landmine detection: the problem and the challenge.
Applied Radiation and Isotopes, 557-563. 7. Joint Improvised Explosive Device Detection and Defeat Organization. (2011).
Counter-IED Smart Book for Pre-Deployment and Field Use. 2. Alexandria, VA, United States of America: Kwickpoint.
8. Kelley, J. (2016). Characterization of Image Quality for an X-ray Backscatter
Radiography System used in the Inspection of Rail Ties. Gainesville: University of Florida.
9. Liesenfelt, M. J. (2016). Development of 2D and 3D Fan Beam X-ray Scatter
Radiography Imaging Methods for Non-Destructive Examination. Gainesville: University of Florida.
10. Meng, C. L. (2008). Computed Image Backscatter Radiography: Proof of Pinciples
and Initial Development. Gainesville: University of Florida. 11. Niemann, W., Olesinski, S., & Thiele, T. (2002). Detection of Buried Landmines with
X-ray Backscatter Technology. 8th European Conference on Nondestructive TEsting (pp. 96-145). Barcelona: Spanish Society for NDT.
12. O'Neill, K. (2016). Discrimination of Subsurface Unexploded Ordinance. Bellingham:
SPIE.
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13. Robledo, L., Carrasco, M., & Mery, D. (2009). A survey of land mine detection technology. International Journal of Remote Sensing, 30(9), 2399-2410. doi:10.1080/01431160802549435
13. Yuk, S., Kim, K. H., & Yi, Y. (2006). Detection of buried landmine with X-ray backscatter technique. ScienceDirect: Nuclear Instruments and Methods in Physics Research, 388-392.
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BIOGRAPHICAL SKETCH
Captain Travis Barker commissioned as a second lieutenant in the United States
Army upon graduating from the United States Military Academy at West Point in 2008.
After completing the Basic Officer Leadership Course III – Infantry in 2009, he deployed
to southern Afghanistan as a member of A Company 4th Battalion 23rd Infantry
Regiment 5th Stryker Brigade Combat Team as a Rifle Platoon Leader for 12 months.
While in Afghanistan, he led his platoon in counter insurgency operations in Kandahar,
Zabul, and Helmand province, where they supported efforts to clear Marjeh with the 2nd
Marine Expeditionary Brigade. Upon returning to the United States, CPT Barker
attended and graduated the Military Intelligence Captain’s Career Course with his wife,
Cathleen. He deployed once again with 1st Battalion, 502nd Infantry Regiment, 2nd
Brigade, 101st Airborne Division to advise elements of the Afghan National Army in
Laghman Province. Upon completing his time as the Heavy Weapons Company
Commander of D Co, 2nd Battalion, 502nd Infantry Regiment, CPT Barker began
pursuing a Master of Science in nuclear engineering in 2015 at the University of Florida,
before moving to the United States Military Academy at West Point to teach in the
Physics and Nuclear Engineering Department with Cathleen. Together they have two
children, Wesley, 7, and Annabeth, 3.