underwater navigation using location-dependent signatures
TRANSCRIPT
978- 1-4577-0557-1/12/$26.00 ©2012 IEEE
Underwater Navigation Using Location-Dependent Signatures
Di Qiu
Sigtem Technology, Inc.
San Mateo, CA
Robert Lynch
Naval Undersea Warfare Center
New Port, RI
Erik Blasch
Air Force Research Lab
WPAFB, OH
Chun Yang
Sigtem Technology, Inc.
San Mateo, CA
Abstract—This paper investigates the benefits of a multisensor
fusion methodology for underwater navigation using location-
dependent signatures, or geotags. The proposed coordinate-
free system uses both natural and man-made signals, as well as
transient events to extract location-dependent signatures for
navigation and guidance. Natural signals include the
geomagnetic field, gravity field, bathymetric features, and
naturally-occurring very low frequency radio signals. Man-
made acoustic sources of opportunity include drainage outlets
and pump stations in littoral zones and particularly in harbors,
which can be explored to serve as underwater beacons for
navigation. This paper models a multisensor coordinate-free
system, characterizes various signals for underwater
navigation, and evaluates the Multisensor Underwater
Signature-based Navigation (MUSNav) system in terms of
accuracy, availability, and continuity of the navigation solution.
Keywords—underwater navigation; multisensor; location signature; geotag; control; MUSNav
TABLE OF CONTENTS
1. INTRODUCTION ………………………………………. 1
2. SYSTEM MODEL ……………………………………… 1
3. SIGNAL CHARACTERISTICS .….……………………… 4
4. PRELIMINARY SIMULATION RESULTS …………….…. 5
5. CONCLUSIONS ………………………………………… 7
REFERENCES ……………………………………………. 8
BIOGRAPHY …………………………………………...…. 8
I. INTRODUCTION
The requirements of performance and complexity of signal
processing for underwater navigation have dramatically
increased over the last several decades. One important
aspect of underwater navigation using location-dependent
signatures, or geotags, is to build and maintain databases of
location-dependent signatures, as shown in Figure 1. The
geotags are compared with real-time acquired signals to
estimate a position location. However, the use of a single
signal for underwater navigation faces problems of poor
resolution and lack of coverage. In contrast, the fusion of
multiple signals has the potential to ensure the required
accuracy, confidence, timeliness, and continuity of the
navigation solution. A multisensor navigation system can
extract more location-dependent features from received
signals and provide high spatial decorrelation in the derived
signatures, thus resulting in high system availability and
reliability. In this paper, we study the spatial correlation (or
spatial decorrelation) of signatures from multiple signals
and the effect these have on search speed and position
location accuracy. Various classifiers developed for
temporal transient signal detection and classification can be
applied for spatial search and localization.
Figure 1 – MUSNav in underwater environment
When acoustic sources of opportunity are available, we
formulate the underwater navigation as a closed-loop
control problem. A guidance control law is derived to
navigate a user based upon the received location-dependent
signatures from its current location to its destination [1].
Possible signatures derived from such sources of
opportunity include differential time-of-arrival (TOA),
differential angle-of-arrival (AOA), and spatial distribution
(gradients) of signal strength and signal power spectra. The
effects of such factors as the geometry of detectable acoustic
sources, and temporal and spatial sampling rates, on
navigation performance will be assessed. A trade study will
help determine the optimal sampling interval and control
gain, leading to an efficient fusion of multiple signals with
the best spatial discrimination for navigation.
The structure of the paper is organized as follows. In
Section II, we first describe the geotag and matching
algorithms for the underwater coordinate-free navigation
system to implement MUSNav, and discuss the control laws
used for system modeling. In Section III, we discuss the
desirable signal characteristics for underwater navigation
and study the temporal and spatial variation properties of
location features from acoustic signals, the geomagnetic
field, and the gravitational field. System performance is then
evaluated in Section IV using simulated acoustic signals. In
Section IV, the trade space between the number of
measurements, position accuracy, computational complexity,
and total path length is studied by varying the defined
control parameters. Finally, concluding remarks are made in
Section V.
II. SYSTEM MODEL
One requirement of MUSNav is that location-dependent
parameters at the destination, or at waypoints, must be
known. Therefore, the MUSNav algorithm developed here
requires a calibration step, or training phase, to obtain the
location-dependent parameters at the destination, which can
be converted to a location signature, or geotag.
A. Geotag Generation
An underwater coordinate-free system requires two steps. In
particular, a training phase and a matching phase. The
geotag-based navigation and positioning technique highly
depends on the initial training phase. The training phase
involves a user receiver collecting location-dependent
parameters at the desired destination, or waypoints, along
the path.
The geotags associated with the trained locations, indicated
as grey dots in Figure 1, are computed based on the
recorded location information and stored in a database for
future use. The second matching phase is then employed for
navigating the underwater vehicle, or vessel, to get to the
destination with the assistance of the trained waypoints. In
the matching phase, the underwater vehicle derives geotags
using received location-dependent parameters, and matches
these with the pre-computed ones in the database to
determine the heading direction. Let Tk be the geotag
derived from the calibration phase at a unique kth
geographic
location and T’ be the geotag derived during the navigating
phase at the same location.
There are many different geotag generation methods, as well
as corresponding matching algorithms. The methods differ
in geotag representation, computational efficiency, and ease
of practical implementation.
In this paper, we apply the MUSNav method [1] that
considers the extracted location-dependent parameter vector
xk = [x1(k), x2(k), …, xn(k)] as a geotag Tk with n elements.
The MUSNav technique is similar to location fingerprinting
except we also use various location-dependent parameters
other than just the received signal strength [2]. There are
two different approaches for the matching process, that is, a
non-parametric approach and a parametric approach.
Non-parametric approach (hard metric). A non-
parametric approach is the nearest neighbor method
(NNM) [3], which is commonly used for indoor
location estimation and pattern matching. The
algorithm calculates the distance between the location
vector measured at a location during verification T’ and
one of the previously stored vectors in the database {Tk,
k = 1, …, K}. A generalized distance metric D(T’, Tk) =
D(x’, xk) is defined in Equation (1) where wi(k) is an
element-wise weighting factor and p is the norm
parameter. For instance, the choice of wi = 1 and p = 2
leads to the Euclidean distance (2-norm).
p
p
ii
n
i i
k kxxkwn
D
/1
1
|)('|)(
11),'( xx (1)
Based on the calculated distances between T’ and a
previously store Tk, the location of a signature that
gives the minimum distance is chosen as the location
for T’ as:
),'(minarg*},...,1{
kKk
Dk xx (2)
It is necessary to set a threshold to guarantee that the
location can be registered at the calibration phase. To
emphasize relative importance of and confidence on
individual location parameters and to account for
correlation between the elements, a modification to
NNM is made, named the weighted nearest neighbor
method (WNNM) [4], which makes use of the
covariance matrix Ck at xk as:
)'()'(),'( 12xxCxxxx kk
T
kkD (3)
Parametric approach (soft metric). A parametric
approach measures distance between location tags with
the help of a Bayesian conditional probability to
determine locations [5]. At the calibration phase, not
only the location parameters but also their covariance
matrix are estimated. The latter is used to help
construct a more robust decision rule for verification.
When the distance between location parameters is
weighted using a Gaussian distribution, we use the
probability density function shown in Equation (4a) to
compare the likelihoods. Since Ck characterizes the
location parameter xk, it only depends on xk subject to
seasonal adjustment to reflect differential effects on the
elements of the location parameter. The location tag Tk
that gives the maximum likelihood is selected for the
measured vector x’ (T’).
)'()'(2
1 1
)det(2
1)|'(
kT
k
ePk
k
xxCxx
Cxx (4a)
When their components are equally important, the
likelihood is given by:
n
i i
ii
i
kk
kxx
knP
12
2
)(2
))('(exp
)(2
11)|'( xx (4b)
B. Multisensor Underwater Signature-based Navigation
We formulate the MUSNav problem as a closed-loop
control problem. A guidance law is derived to guide a
receiver based upon the pre-computed and measured
geotags. The steps to navigate a receiver from one location
to another are given in the flow chart in Figure 2. Once the
receiver computes the geotag associated with the current
location from sensors, the user decides the heading direction
and how far he will move before taking new measurements.
2
The guidance process consists of a first step of coarse
navigation and a second step of vernier navigation, which
are equivalent to coarse acquisition and fine tracking. At the
coarse navigation phase, a receiver first sweeps all
directions, from 0 to 360 degrees, and computes the geotags
at various directions.
Figure 2 - MUSNav flowchart
The heading accuracy depends on the sweeping angle
interval, or the number of geotags around the starting
position. The initial heading direction is the one that gives
the minimum distance from the target geotag. Once the
initial heading is determined, the receiver enters the refined
search or tracking phase, and computes geotags along the
path. These geotags are used to further adjust the heading
towards the destination. The number of computed geotags
depends on the tracking step size, which is the distance from
one measurement to the next.
We define a number of control parameters, which can be
specified by users and are essential to navigation
performance. Figure 3 illustrates the defined control
parameters. The first control parameter is the initial heading
resolution, res. A smaller angular resolution produces a
more accurate initial heading. As the receiver enters the
tracking phase, the parameters of tracking step size rs and
search angle s control the tradeoff between travel distance,
convergence speed, and computational loading.
A finer tracking step size requires more location-dependent
measurements, resulting in a more accurate heading
direction but with a higher computational burden. On the
other hand, less accurate angle information resulting from a
coarse tracking step size might produce a longer trajectory,
or path length, but with a lower computational demand. The
last parameter is the geotag threshold, p, which controls the
desired location convergence. As the MUSNav technique
relies on the Euclidean distance between the stored geotag
and the measurements, the convergence threshold is
important as a tradeoff between convergence speed and
estimated position accuracy.
Figure 3 - MUSNav control parameters res, s, and p
C. Guidance Law
A closed-loop control is utilized to implement the
navigation method to guide and control a receiver’s
trajectory. The feedback control block diagram is given in
Figure 4.
Figure 4 - MUSNav Guidance and control block diagram
The design of an error loop discriminator and a loop filter
characterizes the geotag tracking phase. These functions
determine two most important performance characteristics
of the loop design, which are the loop thermal noise error
and the maximum dynamic stress threshold.
The error discriminators used in this paper are linear and
piece-wise, as shown in Figure 5. The location-dependent
parameter, time-of-arrival (TOA) , is chosen as an example
to illustrate the implementation of the control law on
MUSNav. The loop discriminator, or spatial error
discriminator (SED), can be modeled as = i – d and ,
which respectively are the distance of the parameter
between the current measurement and the target (the
measurements at destination), and its power. The absolute
distance between the current location and the destination, x,
is estimated from the SED as well as the control gain
selected.
The objective of the loop filter is to reduce noise and control
the convergence speed to the desired geotag. As shown in
Figure 4, the output of the loop filter is fed back to the
original input to produce the spatial error. There are many
types of loop filters, each having different characteristics.
For instance, the first order loop filter is sensitive to velocity
stress while the second order filter is sensitive to
acceleration stress. In this paper, we do not focus on the
dynamics of the underwater vehicle. Thus, a simple first
order loop gain is applied.
3
Figure 5 - Linear (left) and piece-wise loop discriminators
III. SIGNAL CHARACTERISTICS
A number of signal sources such as acoustic signals, the
geomagnetic field, the gravity field, and bathymetric
features can be used for the underwater coordinate-free
navigation and guidance. Nowadays, many researches make
use of these natural signals that are not designed for
navigation, rely on location servers, and monitor units to
calibrate the radio-based transmitter timing biases. The
calibration data is provided to users via dedicated data links
[6, 7]. Similar work on cooperative position location using
periodic codes in broadcast digital transmissions was
studied. Being cooperative, the teammates have a means to
communicate to one another via a wireless data link to
coordinate their activities, exchange data, and perform
mutual aiding in the form of cooperative referencing and
calibration [8]. However, this paper does not provide a
solved position fix using conventional methods, but instead
relies on the spatial distribution of a variety of location
features extracted from different systems and sources.
A. Acoustic Signal
The underwater environment, especially in sea water,
induces conductivity, which results in rapid attenuation of
electro-magnetic signals at high frequencies. Thus, acoustic
signals are best supported at low frequencies, and in a
frequency range of between 10 and 15 kHz [9].
Navigation in underwater environments presents a number
of unique challenges due to the complexity of the
environmental characteristics. The background noise,
although often characterized as Gaussian, is not white, but
has a decaying power spectral density. Surface waves,
internal turbulence, fluctuations in the sound speed, and
other small-scale phenomena contribute to random signal
variations, as well as multipath in the received signals. The
presence of Doppler spread impacts acoustic energy in the
sea, due to source/receiver motion, as well as motion of the
water waves that may not be well represented by a simple
Doppler shift. Frequency-dependent propagation losses
result in relative small available bandwidth for acoustic
transmissions, and potentially large delay variations leads to
strong frequency selectivity, which may be time-varying. As
a result, there are no standardized models for the acoustic
channel fading, and experimental measurements are often
made to assess the statistical properties of the channel at
particular testing sites.
The signal propagation from a stationary transmitter remains
relatively stable as location features for a given position.
The resulting spatial discrimination of signal patterns is akin
to standing waveforms produced by reflection, diffraction,
refraction, and scattering of acoustic signals in the
environment. Such a time-invariant property of local-
specific location signatures, also known as fingerprinting,
has been used for positioning [10, 11].
The usable location-dependent parameters extracted from
acoustic signals are signal strength, time-of-arrival (TOA),
time difference of arrival (TDOA), and angle-of-arrival
(AOA). Range measurements as well as differential ranges
between the user and acoustic sources (obtained from
TDOA) are used to determine the user location via multi-
lateration in conventional positioning systems. Similarly,
the user location can also be estimated from AOA
measurements via triangulation. A sector angle is the
difference in AOA between two transmitters. Other possible
features, which are not commonly used in navigation
systems, are short-time energy, spectral flux, and spectral
centroid [12]. The short-time energy of a frame of collected
signal waveforms is defined as the sum of squares of the
signal samples normalized by the same frame length.
Spectral flux is a measure of how quickly the power
spectrum of a signal is changing, and is location-dependent.
The spectral centroid is a measure used in digital processing
to characterize a spectrum. Perceptually, it has strong
correlation with the ―brightness‖ of a sound, and can be
calculated as the weighted mean of the frequencies.
With a particular set of transmitters, the received parameters
are location-dependent and have a unique geographic
distribution. Figure 6 illustrates the color contours of the
geographic distribution of two parameters – differential
range and sector angle. A differential range is the difference
in absolute ranges of two transmitters measured at a receiver,
while a sector angle is the angle formed by two transmitters
and a receiver. Three arbitrary signal sources were chosen
and indicated as s1, s2, and s3. The color contour changes
gradually from red, high amplitude, to blue, low amplitude.
For instance, the receivers on the baseline between two
transmitters have the highest sector angle of 180˚. As a
receiver moves away from the baseline, the sector angle
decreases. The geographic distribution of the parameters
indicates the location-dependent uniqueness, which is
essential to the underwater coordinate-free system. The use
of more usable parameters, as well as a larger number of
transmitters improves the spatial discrimination.
B. Geomagnetic Field
The Earth’s magnetic field [13], also called the geomagnetic
field, is generated within its molten iron core through a
combination of thermal movement, the Earth’s daily
rotation, and electrical forces within the core. Many
research efforts have led to models for the geomagnetic field.
The geomagnetic reference model is the basis for
establishing the declination and its variation across the
surface of the globe. 4
Figure 6 - Geographic distribution of location features:
differential range (top) and sector angle (bottom)
There are a number of geomagnetic field-related features
that can be used for underwater coordinate-free navigation.
The total magnetic field can be divided into several
components:
Declination (D) indicates the difference (in degrees)
between the heading of the truth north and the magnetic
north.
Inclination (I) is the angle (in degrees) of the magnetic
field above or below horizontal.
Horizontal intensity (H) defines the horizontal component
of the total field intensity.
Vertical intensity (Z) defines the vertical component of
the total field intensity.
Total intensity (F) is the strength of the magnetic field.
The intensity and structure of the Earth’s magnetic field
vary both temporally and spatially. The temporal variation is
slow but reflects influences on the flow of thermal currents
within the iron core. As a result, the models of the magnetic
field, as well as the location features for the MUSNav
database, need to be updated periodically. The magnetic
field strength, direction and change rates are predicted every
five years for a 5-year period. Figure 7 illustrates the
geographical distribution of declination generated from the
International Geomagnetic Reference Field (IGRF) model.
Figure 7 - World magnetic chart for declination generated
from 1995 Epoch IGRF model
[Picture courtesy: nationalatlas.gov]
C. Gravity
Gravity, and the associated acceleration produced by the
Earth, varies with latitude, altitude, topography, and geology
[14]. Due to the outward centrifugal force produced by the
Earth’s rotation, and the Earth’s equatorial bulge, latitudes
near the equator have high gravity as opposed to the polar
latitudes. In addition, gravity decreases with latitude as
greater latitudes indicate greater distance from the Earth’s
center. Local variations in topography and geology cause
fluctuation in the Earth’s gravitational field. The spatial
variation of the gravitational field can benefit the design of
coordinate-free navigation and add more spatial
discrimination in the computed geotags or location
signatures.
IV. PRELIMINARY SIMULATION RESULTS
A. Simulation Scenario
In this paper, we use simulated acoustic signals as a case
study to evaluate the performance of MUSNav. The center
frequency of the signal is chosen to be 10 kHz. A simple
analytical propagation model of acoustic signals is used to
estimate the received signal strength at the user’s location
(see Equation (5) below). The location features, which
include differential range, sector angle, and signal strength,
from four emitters are used to compute the geotags. There
are different ways to estimate range or differential range
measurements depending on the acoustic signal architecture.
If a pseudorandom code or a timing sequence is embedded
in the signals, correlation can be applied to detect the
incoming signal and estimate the differential TOA. The
differential range measurements require the different base
stations to be synchronized. Without synchronization,
external timing information, such as GPS, can be used to
calculate the biases between different stations. A coarse way
to estimate ranging information is to convert the received
signal strength to the range with a proper propagation model.
Sector angle measurements can be obtained using either an
antenna array or multiple sensors.
Propagation without obstacles is an ideal case. Several
factors that include reflection, diffraction, and scattering
s1 s2
s3
x [m]
y [
m]
Contours of Differential Ranges to 3 Sources
-1000 0 1000 2000 3000 4000 5000 6000 7000-1000
0
1000
2000
3000
4000
5000
6000
7000
s1 s2
s3
x [m]
y [
m]
Contours of Sector Angles to 3 Sources
-1000 0 1000 2000 3000 4000 5000 6000-1000
0
1000
2000
3000
4000
5000
6000
7000
5
should be taken into account when acoustic signals
encounter obstacles. In this paper, we assume that the path
loss depends on absorption, which is the transfer of acoustic
energy into heat, and spreading loss, which increases with
the propagation distance. The overall path loss can be
written as [9]:
rdd
b
r
fad
dfdA )(),( (5)
where f is the signal center frequency, and d is the
transmission distance taken in reference to some dr. The
path loss exponent b models the spreading loss, and its usual
values are between 1 and 2, with 1 for cylindrical spreading
and 2 for spherical spreading. The absorption coefficient a(f)
can be obtained using an empirical formula [15]. Hence,
Equation (4) is used to derive the received signal strength
for geotags.
B. Noise-free Case
First, we consider the ideal case where there is no noise or
other error sources added to the received location
measurements. Figure 8 plots and compares the estimated
trajectories of a receiver derived from the computed geotags
using differential range, sector angle, and signal strength.
The blue dots represent four emitters. The destination
location is shown as a red star marker. Three different paths
from differential range, sector angle, and signal strength are
given in green, magenta, and black, respectively.
Figure 8. Comparison of location-dependent parameters:
different range, sector angle, and signal strength
Constant control gains are applied for all three cases. The
angle interval for initial probing is ten degrees. The coarse
tracking interval step is 1000 meters, while a fine tracking
interval step of ten meters is used when the receiver
approaches close to the destination. The Euclidean norm is
chosen to compute the spatial error discriminator. A
combination of all three parameters provides the shortest
trajectory (shown as blue line in Figure 8), or path length,
while the sector angle gives the longest.
We next compare the performance of two different error
discriminators: maximum difference and Euclidean norm.
The results are given in Figure 9.
Figure 9. Comparison of two spatial error discriminators:
maximum difference and Euclidean norm
Figure 9(top) shows the trajectories of the receiver paths
using the three parameters and the two error discriminators.
Figure 9 (bottom) summarizes the tradeoff in performance
between path distance and the number of way points, or
equivalently, the computational loads. We observe that the
differential range with a norm discriminator gives the
shortest trajectory distance, and the sector angle with norm
discriminator results in the longest distance. Further, the
differential range with differencing discriminator requires
the highest computational power, and the signal strength
with norm discriminator has the least measurements. A
combination use of different parameters would improve the
spatial discrimination of the computed geotag, producing a
shorter trajectory length [16, 17, 18].
The control gain plays an important role in the convergence
to the target geotag or the destination. We evaluate the trade
-6000 -4000 -2000 0 2000 4000 6000-8000
-6000
-4000
-2000
0
2000
4000
6000
8000
s1
s2
s3
s4
x [m]
y [
m]
User Trajectory
Base stations
Destination
Differential range
Sector angle
Signal strength
All three parametes
-8000 -6000 -4000 -2000 0 2000 4000 6000 8000
-6000
-4000
-2000
0
2000
4000
6000
s1
s2
s3
s4
x [m]
y [
m]
User Trajectory
Base stations
Destination
Diff. range, Norm
Diff. range, Max Difference
Sector angle, Norm
Sector angle, Max Difference
Signal strength, Norm
Signal strength, Max Difference
12 12.5 13 13.5 14 14.5 15 15.50
50
100
150
200
250
Trajectory distance [km]
Nu
mb
er o
f w
ay
po
ints
Diff. range, Norm
Diff. range, Max Difference
Sector angle, Norm
Sector angle, Max Difference
Signal strength, Norm
Signal strength, Max Difference
6
space between the computational demand and trajectory
distance by varying the linear gain, shown in Figure 10.
Figure 10. Linear control gain study
In this study, we use the location-dependent feature,
differential range. The linear control gain varies from 0.01
to 0.4. Similarly, the top plot in Figure 10 gives the
trajectories of the selected control gains, and the bottom plot
shows the tradeoff between the number of measurements
and the trajectory length. The simulation results show that
the linear gain is proportional to the trajectory length but
inversely proportional to the computational power. A faster
convergence system would aim for a smaller control gain,
whereas, a more efficient system prefers a large control gain.
C. Random Noise Case
In practice, there is always random noise and other error
sources that contaminate the received acoustic signals. In
the subsection, we add random noise to the simulated
signals and examine the resulting change in system
performance. The result is illustrated in Figure 11.
Differential range is used in the simulation for the different
noise level comparison. The same location-dependent
parameter, differential range, is used. The linear control gain
is chosen to be 0.1. The standard deviation, , of the
parameter ranges from 1 to 5 meters.
Figure 11. Random noise lowers convergence speed
As expected, the higher the noise floor, the slower the
convergence speed. A large noise floor increases the spatial
error, which delays receiver convergence to the desired
geotag. A longer trajectory requires more measurements of
geotags to tune the heading direction. As a result, random
noise increases computational loading and trajectory length.
One solution to improve the trajectory length, by
minimizing the effects of random noise and other error
sources, is to reduce the linear control gain as illustrated in
Figure 12. The green path represents a linear control gain of
0.01, which is the smallest amongst all gains shown and
gives the shortest path length. Aforementioned, the tradeoff
of using a small control gain is between high computational
loads or more location measurements along the path. The
use of a small gain is equivalent to tightening up the noise
bandwidth. Although the path length is reduced, the number
of way points is increased, which leads to a longer time to
reach the desired destination.
Figure 12. Reducing linear control gain improves the
convergence speed
-8000 -6000 -4000 -2000 0 2000 4000 6000 8000
-6000
-4000
-2000
0
2000
4000
6000
s1
s2
s3
s4
x [m]
y [
m]
User Trajectory
Base stations
Destination
Control coeff.=0.01
Control coeff.=0.1
Control coeff.=0.2
Control coeff.=0.3
Control coeff.=0.4
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.40
200
400
600
Nu
mb
er
of
way
po
ints
Linear control coefficient
Tradeoff Analysis
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.412
14
16
18
Tra
jecto
ry d
ista
nce [
km
]
-1 -0.5 0 0.5 1
x 104
-6000
-4000
-2000
0
2000
4000
6000
8000
s1
s2
s3
s4
x [m]
y [
m]
User Trajectory
Base stations
Destination
No noise
= 1m
= 2m
= 3m
= 4m
= 5m
-8000 -6000 -4000 -2000 0 2000 4000 6000 8000
-6000
-4000
-2000
0
2000
4000
6000
s1
s2
s3
s4
x [m]
y [
m]
User Trajectory
Base stations
Destination
Control coeff.=0.01
Control coeff.=0.02
Control coeff.=0.05
Control coeff.=0.08
Control coeff.=0.1
7
V. CONCLUSIONS
We formulated a multisensor underwater signature-based
navigation (MUSNav) technique that uses location-
dependent parameters from the received acoustic signals in
underwater environments. Instead of providing a position
fix such as longitude, latitude, and altitude, the method
guides a receiver using the trained destination geotags and
the location measurements along the path. A combined use
of acoustic signals, the geomagnetic field, and the
gravitational field can improve the spatial discrimination of
derived geotags as well as navigation system performance.
The proposed MUSNav algorithm supports a wide range of
location-base applications, for example, animal tracking.
Figure 13 shows the recorded path of migrating sea turtles,
which are believed to perform long-distance navigation
using geo-magnetic field based compass sensing and map
sensing [19, 20]. The actual routes strongly resemble the
above simulated trajectories.
Figure 13. Trajectories of migrating sea turtles [20]
A training or calibration phase is required to implement
MUSNav. The geotag associated with the destination is
stored in a database for future matching. A closed loop
control law is used to formulate navigation and guidance.
The process consists of a coarse acquisition to determine the
heading direction and fine tracking to approach the
destination. Both the error discriminator and loop filter are
essential to reduce the noise and increase convergence speed.
We evaluated the MUSNav algorithm using a simulated
acoustic signal as a case study. Several trade spaces were
studied and analyzed. The total path length, computational
load, and position accuracy can be traded off against each
other by varying the control parameters, angular resolution,
tracking steps, and the threshold for convergence. In
addition, we observed that the trajectory length is
proportional to the linear gain whereas the computational
complexity is inversely proportional to the linear gain.
A combined use of various location features such as TDOA,
signal strength, sector angle, gravity, and magnetic field
strength can significantly improve the spatial discrimination
of the computed geotags. As a result, we can achieve better
precision in the final estimated target location. However, the
use of more location features increase the probability of
failure due to the increased number of error sources,
especially in underwater environments. Examples of error
sources are random noise, biological noise, noise generated
from ships, obstacles inside the water, and temporal change
of water waves among others. In addition, there are errors
originating from acoustic transmission system operations.
Acoustic transmitters might be offline due to maintenance
or other implementation issues. As a result, a geotag will not
be reproducible when there is an insufficient number of
location features received.
Such practical issues as operational continuity and
likelihood of failure to map into the desired geotag, will be
further studied by developing error-tolerant algorithms to
reduce the system risk, thus increasing the robustness of the
computed geotags. We will evaluate such errors in both
Euclidean distance and Hamming distance and develop
error-tolerant algorithms that can account for various types
of error sources. In addition, we will compare the parametric
and non-parametric geotag generation approaches in our
future simulations.
Simulations provide us with the insights from an analytical
point of view. The practical aspects of signal processing and
system implementation are better understood using real data,
which is our future plan to implement and test our approach.
Moreover, we will investigate and use more location
features, such as geomagnetic field, to improve the spatial
discrimination of computed geotags.
REFERENCE
[1] C. Yang and D. Qiu, Geo-coordinate Free Radio
Navigation and Guidance Using Measured Location-
Specific Parameters, U.S. Patent Application No. US
61/39618, 2010.
[2] P. Bahl and V. N. Padmanabhan, ―RADAR: An in-
building RF-based user location and tracking system,‖
IEEE Infocom 2000, Tel Aviv, Israel, 26-30 Mar 2000,
vol. 2,pp. 775-784.
[3] T. Roos, P. Myllymaki, H. Tirri, P. Misikangas, and J.
Sievanen, ―A probabilistic approach to WLAN user
location estimation,‖ Inter. J. of Wireless Information
Networks, vol. 9, no. 3, pp. 155-164, July 2002.
[4] S. Saha, K. Chaudhuri, D. Sanghi, and P. Bhagwat,
―Location determination of a mobile device using IEEE
802.11b access point signals,‖ IEEE Wireless
Communications & Networking Conference (WCNC),
New Orleans, Louisiana, US, Mar 16-20 2003, vol.3, pp.
1987-1992.
[5] Xia, H. H., "A simplified model for prediction path loss
in urban and suburban environments," IEEE Trans. Veh.
Technol., Vol. 46, No. 4, 1040-1046, Nov. 1997.
8
[6] M. Rabinowitz and J.J. Spilker, Jr., ―A New Positioning
System Using Television Synchronization Signals,‖
IEEE Trans. Broadcasting, 51(1), 51–61, March 2005.
[7] T. Young, ―TVGPS, A Hybrid Location and Timing
Solution for GPS-Challenged and GPS-Denied
Environments,‖ Proc. of the ION-JSFE JNC 2009, June
2009, Orlando, FL.
[8] Y. Zhao, Vehicle Location and Navigation Systems,
Artech House, Boston, MA, 1997.
[9] M. Stojanovic and J. Presisig, ―Underwater acoustic
communication channels: propagation models and
statistical characterization,‖ IEEE Communications
Magazine, Jan 2009.
[10] R. Schmidt, Andre Gagnon, and D. Allcock, ―Maritime
Containers Tracking Trial Results,‖ IEEE AES
Magazine, Sep 2009.
[11] H.R. Everett, Sensors for Mobile Robots: Theory and
Application, AK Peters, Ltd., Wellesley, MA 1995.
[12] G. Padmavathi, D. Shanmugapriya, and M. Kalaivani,
―Acoustic signal based feature extraction for vehicular
classification.‖ IEEE Advanced Computer Theory and
Engineering (ICACTE), Aug. 2010.
[13] G. Hulot, et. al., ―The magnetic field of planet Earth,‖
Space Science Reviews, Vol. 152, Issue 1-4, pp. 159-
222, Sep 2010.
[14] R. Forsberg, M.G. Sideris, and C.K. Shum, ―The
gravity field and GGOS,‖ Journal of Geodynamics 40,
pp. 387-393, 2005.
[15] L. M. Berkhovskikh and Y. Lysanov, Fundamentals of
Ocean Acoustic, Springer, 1982.
[16] D. Qiu, R. Lynch, and C. Yang, ―Location-Dependent
RF Geotags for Positioning and Security,‖ Proc. SPIE,
Vol. 8061, 2011.
[17] D. Qiu, M. Miller, S. DeVilbiss, T. Nguyen, and C.
Yang, ―Geo-Coordinate Free Guidance and Navigation,‖
ION-GNSS, Portland, OR, Sept. 2010.
[18] C. Yang, T. Nguyen, M. Miller, and D. Qiu, ―Multi-
scale Location-Specific Features for Positioning and
Navigation: Bio-Inspired Navigation,‖ Autonomous
Weapons Summit & GNC Challenges for Miniature
Autonomous Systems Workshop 2010, October 2010,
Fort Walton Beach, FL.
[19] S. Johnson and K.J. Lohmann, ―The Physics and
Neurobiology of Magnetoreception,‖ Nature Review:
Neuroscience, Vol. 6, Sept. 2005.
[20] URL:
http://www.deee.unipi.it/islameta/Sea%20Turtle%20Na
vigation.html.
Di Qiu received her B.S. from University of
California, Los Angeles, in 2003, and her
M.S. and Ph.D. both in Aeronautics and
Astronautics Engineering from Stanford
University in 2004 and 2009, respectively.
She has worked on ionospheric threat
modeling, signal authentication, location-
based security modeling and demonstration,
information theory, and parametric fuzzy extraction. Dr.
Qiu’s current research interests include navigation using
signals of opportunity (SOOP), sensor data fusion, state
estimation, and pattern classification.
Robert Lynch is a senior research scientist
with the Naval Undersea Warfare Center
in Newport, RI. He holds BS and MS
degrees, both in Electrical Engineering,
from Union College, Schenectady, NY,
and a Ph.D. in Electrical Engineering from
the University of Connecticut, Storrs, CT.
His research interests are in the areas of
pattern recognition and classification, detection, data fusion,
tracking, and signal processing. Dr. Lynch is Vice President
Communications of the International Society of Information
Fusion, and is Managing Editor of the Journal of Advances
in Information Fusion. Dr. Lynch is a Senior Member of the
IEEE, and is an Associate Editor of the IEEE Transactions
on Systems, Man, and Cybernetics Part B, Cybernetics. He
is a former recipient of the NAVSEA Excellence in Science
Award, and the Federal Laboratory Consortium’s
Excellence in Technology Transfer Award. Dr. Lynch is an
Adjunct Lecturer in the Electrical and Computer
Engineering Department at the University of Connecticut.
Erik Blasch is a Fusion Evaluation Tech
Lead for the Air Force Research
Laboratory, Rome, NY and a
Reserve Officer at the Air Force Office
of Scientific Research (AFOSR). He
received his MSEE (1997) and Ph.D. in
Electrical Engineering (1999) from
Wright State University, a MS in Mech.
Eng (1994) and MS in Industrial Eng.
(1995) from Georgia Tech, and a BSME from MIT in 1992
among other advanced degrees in engineering, health
science, economics, and business administration. He is a
past President of the International Society of Information
Fusion (ISIF), a member of the IEEE AESS Board of
Governors, a SPIE Fellow, and active in AIAA and ION.
His research interests include target tracking, sensor and
information fusion, automatic target
recognition, biologically-inspired robotics, and controls.
Chun Yang received his Bachelor of
Engineering from Northeastern
University, Shenyang, China, in 1984
and his title of Docteur en Science from
Université de Paris, Orsay, France, in
1989. After two years of postdoctoral
research at University of Connecticut,
Storrs, CT, he has been with Sigtem Technology, Inc. since
1994. He has been working on adaptive array and baseband
signal processing for GNSS receivers and radar systems as
well as on nonlinear state estimation with applications in
target tracking, integrated inertial navigation, and
information fusion. Dr. Yang is an Adjunct Professor of
Electrical and Computer Engineering at Miami University.
He is the member of the ION, IEEE, ISIF, and SPIE.
9