2005 7th International Conference on Information Fusion (FUSION)

Proactive Decision Fusion for Site SecurityErik Blasch

Air Force Research Lab2241 Avionics CirWPAFB, OH 45433

erik.blasch(wpafb.af.mil

Abstract - Current urban operations require intelligentmethods for integrating data and transmitting fusedinformation to users. The ground user requires data onimmediate threats for rapid reaction, whereas acommander has time to reason over information onpotential threats for preventive action. Using predictedinformation affords proactive decision making onanticipated threats. Proactive action includes gatheringnew information, relocating for safety, and hindering theopposition from action. Complexities abound with urbanoperations and sensor fusion strategies must deliverquality information (i.e. timely, accurate, confident, highthroughput, and minimal cost). New strategies are neededto account for high density targets, sensor obscurations,and rapid response to meet Sustainable and SecurityOperations (SASO). The purpose of this paper is toevaluate the inherent reliability of the fusion system todeliver a consistent and succinct set of information overthe appropriate time window. This paper with highlight(1) proactive use ofsensor resources, (2) force protectionrisk mitigation by integrating users with fusion system,and (3) communication and decision making modeling toassess operational timeliness needs.

Keywords: Fusion, Detection, Urban Operations,Adversarial Decision Making, Risk, Throughput

1 IntroductionRecent events have changed domain applications for

multisensor information fusion and target tracking.Increasingly complex, dynamically changing scenariosarise, requiring more intelligent and efficient processingstrategies. Integral to any information processing isdecision making (DM). Many such processing strategiesare embedded within security systems, and must berigorously evaluated by a standardized method overvarious locations, changing targets, or unknown threats. Itfollows that all DM processing, and any DM affected byan output of a subsystem, must receive intense scrutiny inperformance evaluation.

"Homeland defense" incorporates many researchissues in intelligent processing, sensor development, andinformation fusion strategies. A scenario of interest forhomeland defense is an urban setting for Sustainable andSecurity Operations (SASO). SASO research is looselydefined as the strategies that can provide protection. For

Susan PlanoDept. ofBiomed. Indust., and HF Engineering

Wright State UniversityDayton, OH 45435splano@ieee.org

instance, the best defensive strategy could mean a goodoffense: placing sensors in appropriate positions toprevent action. A reasonable protection strategy is one thatprovides security over a variety of scenarios and operatingconditions. Figure 1 shows an urban SASO defensestrategy that incorporates checkpoints for individual andvehicle identification (ID), surveillance unmanned aerialvehicles (UAVs) for target tracking, and audio reports.

A

Figure 1. Urban Setting

Urban environments are now recognized as presentingnon-conventional adversarial situations, which vary withthe operating conditions. However, there are commonthemes associated with urban environments, such as densepopulation and buildings, roads and infrastructure, andclosely spaced military and civilian vehicles. Urbanscenarios require many novel fusion applications that needinvestigation, such as evaluation metrics, proactive controlof sensors, distribution of information to users, andusefulness of pedigree information. To determinesuccessful intelligent processing strategies, an evaluationassessment [1] is needed to determine if developmentsenhance homeland defense fusion systems capabilities.

In this paper, we show an evaluation methodology forthe analysis of a fusion system. Section 2 reviews thecurrent state for sensor, or data, fusion performancemetrics, and the critical requirements for effective decisionmaking. Section 3 discusses reactive, proactive, andpreventive DM, and, specifically, the time windowassociated with the DM cycle. Section 4 presents aqueuing analysis for evaluating system timeliness, aquality of service (QoS) metric. Sectiorl 5 details systemlevel performance metrics. A simulation is summarized inSection 6. Section 7 draws conclusions.

0-7803-9286-8/05/$20.00 ©2005 IEEE 1 584

2 Information Fusion EvaluationSASO decision making presumes an adversarial

environment. Designing complex and often-distributeddecision support systems-which process data intoinformation, information into decisions, decisions intoplans, and plans into actions-requires an understandingof both the fusion processes and the decision makingprocesses. Important aspects of fusion include timeliness,mitigation of uncertainty, and other quality aspects ofoutputs. Decision making contexts, requirements andconstraints add to the overall system constraints.Standardized metrics for evaluating the success ofdeployed and proposed systems must map to theseconstraints and other essential requirements to scores.

Because every stage of security analysis has inherentdelays-in receiving sensor information, in presenting afused information picture to the user, and in theinformation user's processing capacity-the entire systemoperation must be evaluated, in addition to the unit testingof fusion components or decision making subsystems. Thebottom-level component in the human-system operation iscomprised of data from the sensors. "Data fusion" is aterm used to refer to the bottom-level, data-driven fusion,whereas "information fusion" refers to processing ofalready-fused data, such as from primary sensors orsources, into meaningful and preferably relevantinformation to another part of the system, human or not.This study primarily focuses upon sensor, or primarysource, data in a security setting, yet much can begeneralized to other applications and fusion levels.

2.1 Designing systems to use fused data successfullyNew technologies continually arise to increase threat

detection. That is fortunate, because it is generally agreedthat new generations of "smart sensors" are criticallyneeded to facilitate adequate security analysis. A smartsensor is either a single sensor or a subsystem of differentsensor components coordinating to provide not just data,but intelligent algorithmic output to aid or conductdecision making in the larger system. Thus, researchersare prototyping and fielding new combinations of existingand novel sensors, such as infrared detectors (IR), electro-optical (EO) vision cameras, audio vehicle ID and speechrecognition platforms (A-ID), and Radio FrequencyIdentity (RFID) tags. Clearly, the high-stakes goals ofsuch efforts depend on optimizing the complementation ofeach sensor's spectral and temporal data capabilities.

The combination of sensor data has to be delivered toa computer for processing and, ultimately, displayed to auser as information for decision making. Human users in asecurity strategy could be homeland protection police,soldiers, or situation monitors and may be an additionalsensor in the analysis [2]. Past fusion efforts such as theNRL BARS system, shown in Figure 2, prototyped fuseddisplays using IR sensors with GPS-derived 3D maps and

time-sensitive mobile element intelligence [3, 4]. Suchmilitary systems lend to SASO.

Figure 2. Battlefield Augmented Reality System (BARS),a wearable fusion system by NRL [3,4]

Another fusion campaign is the ongoing attempt toemploy face recognition with other strategies in high-traffic and high-risk milieus. The volume oftargets to scanrequires not just the fusion of sensor data, but also 1)information fusion from multiple targets' data, to detecte.g., group features, or questionable individual behavior inthe context of multi-person interactions, or compared toother people in the immediate vicinity, or to others'behavior in similar circumstances; and 2) queuing of anysuch inputs to the analysis process for the operator toretrieve later. Missed opportunities are a great concem inall security monitoring applications-missed securityopportunities confer advantage to the adversary.

Figure 3 is a sample montage posed by Steve Mann[5], of MIT, regarding wearable devices used, in this case,for face recognition on the go. Alan Alda is the model,who not coincidentally narrated a further body of facerecognition research on PBS' Scientific AmericanFrontiers in 2001 [6], about the neuroscience behindnatural face recognition and disorders of face recognition.Researchers are advocated to review lessons leamed fromadvances in understanding how the brain performs thiscomplex task efficiently, as well as about the points atwhich it can seemingly go awry, and apply these lessonsto security system design and evaluation. The imagesequence and algorithm displays easily conjures the sceneofroving personnel or robots scanning urban crowds.

(.) (b) kFigure 3. Identifying faces in high trafficked location [5].

Standardized performance and quality of service(QoS) metrics are sine qua non for evaluating every stageof data processing and subsystem hand-off of data andstate information. Without metrics, proper scientificevaluation method cannot proceed across myriad anddisparate proposed systems having high complexity and

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criticality. A chief evaluation aim regarding any system isthe ongoing facilitation of adequate situation awareness(SA). SA is not automatically guaranteed for the operatorrelying on new fused hybrid sensor systems. Even thoughthese seem to promise much desired increases in capacity,data acuity (sharpened resolution, presumably resolvingmore information from noise), and timeliness among theQoS metrics, the human cognitive process capacity is abottleneck in overall process operation. The detriment thusposed to SA maintenance occurs at the perception level ofSA, Level 1 in the most frequent characterization [7].Detriments at this level account for 77% of SA relatederrors [8], as well as indirectly affecting DM acuity atlevels 2 and 3, called comprehension andprojection.

Furthermore, SASO depends not only uponindividuals making decisions, or single subsystemsgenerating analyses. Rather, team communication andteam-based decision making have always been integral tocomplex operations, whether civilian or military, tacticalor strategic, municipal, federal, or inter-governmental.Team communication takes many forms across operationalenvironments and changes dynamically with newsituations. Communication and DM can be joint,allocated, shared, etc., and this requires the maintenanceof team situation awareness. This is clearly paramount insecurity analysis in high-stakes adversarial environments.Cognitive processes, human or machine equivalent

'smart' algorithms, require non-trivial amounts of time toreach a decision, at both the individual and team levels.This applies, analogously, to both the individualcomponent and inter-component/subsystem levels of themachine-side DM. DM duration will either run shorterthan the inter-arrival time interval of data generated bysensors, leading to starvation of the DM process, or elseDM duration will run longer than the interval betweennew data arrivals. Both cases create situations prone toerrors, whether the DM operator is human or machine.The former case has consequences of starving the human'scognitive flow, including vigilance waning as attentionwanders, and the dissipation or overwriting of informationchunks, held in short term memory, which could havebeen relevant to later-arriving data. In the latter case, dataarrives while the information processing, or DM process,operator is occupied, so this data often "balks" out of thesystem without being used by that operator. This occurswhen systems are not adequately designed to hold inreserve the excess inputs to an overburdened process.These excess inputs balk out of the system if there is nobuffer in which to queue these, or if the buffer is so smallas to overwrite data. The queue is necessary to preservethe data inputs until the downstream analysis and DMprocess becomes available again. In either case, theimmediate deficit takes the form of missed data. If this lostdata is especially salient to the overall mission of the DMprocess it would have fed, then more sophisticated systemdesign is required beyond data fusion and presentation.

The operator and team must be able to access data invarious ways, even after the arrival window closes.Therefore, beyond the already-complex science of datafusion, information processing and analysis systems mustnot only queue and index that fused data, but be built uponmodels of the actual work and DM that occurs at eachstage in real operational scenarios. In manufacturing, inputmaterials for an occupied process server are queued in astorage buffer, which must be feasibly and reliablyaccessible when the operator is available and needs thequeued input. Adversarial security scenarios, as withmilitary scenarios, have additional complexities farbeyond that simple manufacturing assembly analogy.Simply ensuring that fast-arriving data does not balk outof the system is not enough. For complex DM, a strictlinear-time availability of fused data, whether immediateor delayed in a queue, is not necessarily the most effectivemethod to feed an information analysis process. ConcertedDM systems design for security must address specificcontext and constraint requirements. This design goalsounds deceptively intuitive. Implementing a design andevaluation methodology is far from trivial.

2.2 Sensor fusion evaluation

Dynamic decision making requires, minimally, threecomponents: (1) situation awareness, (2) dynamicresponsiveness to changing conditions, and (3) continualanalysis to meet throughput and latency performancerequirements. These three factors are instantiated by atracking and ID [9] system, an interactive display to allowthe user to make decisions, and metrics for replanning andsensor management.[1] To afford interactions betweenfuture information-fusion system (IFS) designs and usersinformation needs, metrics are required. In this paper, weuse the User-Fusion model metrics to understand thetimeliness of information for decision making. The metricschosen include timeliness, accuracy, throughput,confidence, and cost. These metrics are similar to thestandard QoS metrics in communication theory and humanfactors literature, as shown in Table 1. [1]

Table 1: Metrics for various Disciplines.

COMM Human Info Fusion ATR/1) TRACKFactors

Delay Reaction Timeliness Acquisition Update RatTime /Run Time

Probability Confidence Confidence Prob. (Hit), Prob. ofof Error Prob. (FA) DetectionDelay Attention Accuracy Positional Covariance

Variation AccuracyThroughpu Workload Throughpu No. Images No. Targets

Cost Cost Cost Collection No. Assetsplatforms

Stallings Wickens, Blasch, Blasch, 1999 Hoffmnan2002 1992 2003 2000

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3 Decision Making: Security MilieusThe objectives of a SASO are to protect coalition

forces, provide safety to the local population andinfrastructure, and mitigate civil unrest (keep the peace).As with unconventional warfare, there is an aspect ofunpredictability that SASO strategies must address.Unanticipated threats may present at any time. Whilehuman expert DM is not completely thwarted by novelengagements, non-adaptive algorithmic and paradigmaticDM is certainly hampered. Any deficit to DM has thepotential to create an untenable adversarial advantage. Theemerging paradigms for preparedness are proactive andpreventive positioning. To illustrate this concept, considerthat while known entities and event categories can beexplicitly designed into system "knowledge" base (SKB),unknowns can only be indirectly represented in the SKB.Only known and expected RFIDs, thermal signature data,and financial transaction patterns, for example, can bedirectly treated algorithmically. Novelties must be treatedproactively by putatively maximizing the potential of a

technique and tool arsenal, one technique of whichemploys the tools adaptive IF. We call these maxima thesurveillance vantage points, or "positions", of the humanor machine decision maker subsystems.

Given that proactive and preventive urban operationshave a similar foundational set of requirements forcommunications, employment of new sensor technologies,and managing interactions in complex dynamic andsurprising environments, evaluation starts with a constructof generally applicable metrics. Adding to the generalframework, we must create an evaluation analysis thatmeasures responsiveness to unanticipated states, sincethese are often the first signals of relevant threats. Acomparative performance analysis, for future applicationsrequiring intelligent computing, will look at objectrecognition, speaker ID, signal and image processing, andprocess control in realistic environments. In this case, we

are interested in the detection of events and state changes.These feed an intent-based tracking system to aid a DMuser in discerning potential threats.

3.1 DM Modes: Reactive, Proactive, Preventive

When tasked with analysis to discern threats, the DMuser can respond to the idea that there might be a certaintype of unanticipated threat by one of three manners,

broadly: reactive, proactive or preventive, as shown inFigure 4. In a Reactive mode, the user makes a rapiddetection and minimizes damage or repeat offense. An IFSwould gather information from a sensor grid detection ofin-situ threats and is ready to act. In this model, the systeminterprets and alerts users to immediate threats. Theindividual user selects the immediate appropriate response

(in seconds) with aid of sensor warnings of non-lethal or

lethal threats.

In the Proactive mode, the user utilizes sensor data toanticipate, detect, and capture needed information prior toan event. In this case, a sensor grid provides surveillancebased on prior intelligence and predicted targetmovements. A Multi-INT sensor system could detect andinterpret anomalous behavior and alert an operator toanticipated threats in minutes. Additionally, the directedsensor mesh tracks individuals back to dwellings andmeeting places, where troops respond quickly and capturethe insurgents, weapons, or useful intelligence.

Sensors ------------------------------ Soldier

Data Info KnowledgeDaaFon Pattern Analysis

tTrgic: Wing Relationships

Sensor CommanderTasking

Backward ForwardReasoning Reasoning

Reactive Preventative

Figure 4. Reasoning in proactive strategies

The mode that captures the entire force over a periodof time (i.e. an hour) is the Preventive Mode. To preventpotential threats or actions, we would (a) increaseinsurgent risk (i.e. arrested after being detected), (b)increase effort (i.e. make it difficult to set up an IEDwithout being detected), and (c) lower payoff of action(i.e. reduce the explosive damage). The proactive modeincludes an Intel database to track events before theyreach deployment. With increased overt surveillance (e.g.UAVs), a system would decrease insurgent opportunitiesto strike or demonstrate (increased risk). Our rapidresponse to recent or developing incidents, reduces theirmotivation (risk too high, payoff too low).

Successful proactive tracking and fusion requires (1)an understanding of individuals and their habits, (2) adetailed knowledge of the threat (user sets up an IED)locations, and (3) models of the routine activity pattern,which frames everyday life in that particular environment.IFS utilization would provide advanced detectioncapabilities to a deployed user whether on foot or at acommand center. The integration of decision modes toperceptual views is shown in Figure 5. The differingdisplays for the foot soldier or command center wouldcorrelate with the decision making mode of interest. Forthe reactive mode, the user would want an actual locationof the immediate threats on a physical map. For theanticipated threats, the user would want the predictedlocations of the adversary and the range of possibleactions. Finally, for the potential threats, the user couldutilize behavior analysis displays that piece togetheraggregated information of group affiliations, equipmentstores, and previous events to predict actions over time.These domain representations were postulated by Waltz[10] as cognitive, symbolic and physical views thatcapture differing perceptual needs.

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mode, we must ensure that the intentions are wellsubstantiated in order to act, which would require a verylow PFA, as shown in Figure 7.

Operator Adversary

Decid Act _Obe Orient

TimeFusio Wlndow Fusion

Orient Observe * _ Act Decide

Figure 6. Operator and adversary OODA loops

1.0

Figure 5. Categories ofAnalytic Views

3.2 Decision Making Cycle

Intelligent decision making employs many

knowledge-based information fusion (KBIF) strategiessuch as neural networks, fuzzy logic, Bayesian networks,evolutionary computing, and expert systems. [11] EachKBIF strategy has different processing durations.Furthermore, each strategy utility differs in the extent towhich it is constrained by the facility with which the user-fusion system may employ it. Observe-orient-decide-act(OODA) loops helps to model a DM user's planned,estimated, or predicted actions. Assessing susceptibilitiesand vulnerabilities to detected/estimated/predicted threatactions, in the context of planned actions, requires a

concurrent timeliness assessment. Such assessment isrequired for adequate DM, yet is not easily attained.

This is similarly posed in the Endsley model of SA:the "projection" level 3 of SA maps to this assessmentprocessing activity [8]. DM is most successful in thepresence of high levels ofprojection SA, or high accuracyof vulnerability or adversarial action assessments. DM isenhanced by correctly anticipating effects and statechanges that will result from potential actions. The nestednature of effects upon effects creates difficulty in makingestimations within an OODA cycle. For instance, effectsof own-force intended courses of action (COAs) are

affected by the decision-making cycle time of the threatinstigator and the ability to detect and recognize them.

3.3 User-Fusion Decision Making

Figure 6 (a) shows the time window associated withmultiple DM entities (operator and adversary). We mustaddress the differing response times associated with eachdecision maker within their OODA cycle. For the reactivemode, since the time window is immediate, the ATR PDdecision can accept a large PFA. For the proactive mode,since anticipated threats are being assessed, it would bemore important to decrease PFA. Finally for the preventive

Prob. ofDetection

Pi,

o0o Probability of False Alami PA 1.0

Figure 7. Detection analysis for modes ofopportunity.

As an example of differing action cycles, Figure 8shows the need for rapid decision making. On the left, ifthe user reacts to immediate threats, where the adversaryhas already been able to attack. Ifthe proactive strategy isused and sensed information details anticipated events, theuser could inter potential threats from occurring. Finally,on the far right, ifthe OODA loop (sensing and processingcombined with behavior analysis) provides the user with apriori information. In this scenario, it would allow theuser to act very quickly, with a low PFA, to prevent actionsfrom occurring, hence inducing protection.

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Figure 8. Progression from reactive to preventive actions.

In order to model the OODA cycle, we utilize queuinganalysis to assess prevention and proactive protection.

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4 Decision Making Proactive Analysis

4.1 Event Detection

Suppose the number of measurements from sensors (s= 1, ... , S) is n 5. Each sensor s measures only an event fora given azimuth angle 0 j of each potential event E, withcoordinates (XE, YE). The position information detectionhas an assumed PD. Some false alarms, PFA, will resultfrom false detections. We will use the number ofpixels ontarget n p, where the detection PD is a function of targetsize T for a given range, r. The pixel number is assumed tobe proportional to target size over distance, np - T/r2.Furthermore, detection is a result of the orthogonal cameraalignment to the event. If the camera is orthogonal to theevent, such as UAV looking downward, then it is more

likely to detect the event, PD -C n p*cos (0 ,j). If the camerais aligned with the event, then the resulting number ofpixels is PD -c n p. Suppose that the fused image collectedis processed using a wireless communication system, were

the images segmented into and delivered as packets p. Thepacket arrival can be modeled as Poisson process.Assuming that the packets arrive in order and that theuseful pixels are anywhere in the image (uniform), thenthe event detection is PE = n p I p.

4.2 Multiple Queuing analysis

Queuing analysis has been used for communication,manufacturing, and civil modeling for discrete eventsimulations. The key determinant is the coefficient X.

Pixel packets arrive to the user at some average rate(pixels / per second) X. At any given time, a certainnumber of pixels will be waiting in the network queue(zero or more); the average number waiting is w, and themean time that a packet must wait is T,. T, is averagedover all incoming pixels, including those that do not waitat all. The fusion processor handles incoming pixels withan average service time T,: this is the time intervalbetween the pixel arrival and the decision. Utilization, p,is the fraction of time that the fusion system is busy,measured over some interval of time. Finally, twoparameters apply to the system as a whole. The averagenumber of pixels in the communication system, includingthe pixel packet being served (if any) and the pixelswaiting (if any), is r. The average time that pixels spend inthe system, waiting and being served is Tr; we refer to thisas the mean queuing/processing time.

If we assume that the queue capacity is infinite, thenno pixels are ever lost from the system [Pr(error) = 0];they are just delayed until they can be served. Under thesecircumstances, the processing rate equals the arrival rate.As the arrival rate, which is the rate of message trafficpassing through the system, increases, the utilizationincreases. At p = 1, the server becomes saturated working

100% of the time. Thus, the theoretical maximum inputrate that can be handled by the system is A = lIIT.

Consider a UAV detecting events. Over the course ofthe day, the UAV observes that on average 32 Image perhour. The wireless network assessment finds that onaverage an Image resides inside the network for 12minutes. Using Little's formula, (p = A T,s), the fusionsystem deduces that there are on average 6.4 Imagesinside the net at any given time (6.4 = 32 Images/hour x0.2 hour/Image). If there were multiple (N = 3) UAVSlooking from different perspectives, with more images onthe net, but simultaneous processing at the fusion center,there is a higher chance to detect the target. Thenp = (XT5)/N , which results in 2.1 images in the net. Thismodeling assumes that more UAVs would result in fasterresponse time to event detection as fewer images aredelayed in the wireless network.

4.3 Response Time analysis

A response time analysis can be made to determinewhen the decision should take place. For the responsetime, we look at the ability to make decisions, where X isthe arrival of detected events and processed by the user, ,is the decision rate of the adversary, and p = A / p is theutilization. p indicates the ability of the user to act fasterand utilize the information before the adversary (larger p).We assume that A is Poisson-distributed, and theProbability of events is

Pr[E items arrive in T] = Pr[X= E] = (A)E = eX (1)

where the events detected and processed by the operatorover the OODA loop cycles are:

P(E.,) = e - [A°° + AOD +XDA + AAO ] T (2)

and for the each part ofOODA cycle (Figure 9),

P(Eop) = e -oo too + e -XOD tOD + e -XDA tDA + e - XAO tAO

t 00 t ime

Figure 9. User-Adversary OODA modeling.

For the adversary:

P(EAD) = e-lOOtOO + e-PODtOD + e-1DAtDA + e-PAOtAO (3)

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Figure 9 shows the interactive OODA cycles, wherethe exponential decay of uncertainty is the result of newdecision making at each cycle. For instance, as newinformation arrives from an observation, it quickly orientsthe user. However, the information saturates and the usermust make a decision. After the OD decision is made, theuser takes action by proactively asking for new data.

The key is to model the time responsiveness ofinformation in the OODA cycle. If multiple UAVs withvarious sensor information was available to an IFS, thenthe user would be afforded increased responsiveness,effectively decreasing the 00 cycle (as shown in FigurelOa). This reduction in time affords the user time toproactively anticipate action and act before, or with, theadversary. Likewise, if the IFS system processes and fusesinformation, this would save allow the user to act in apreventive mode, before the adversary can act. Figure lObshows the case where the user acts before the adversarymakes a decision to act. .

I.I

too toD tDA too Time

Figure 10. Response time analysis for multiple operators.

Ifwe look at the modeling analysis postulated, we candetermine the effectiveness of the IFS to aid the user indecision making. Since we want proactive and preventivestrategies, we assess the capability of the IFS to increaseinsurgent risk, increase effort, and lower adversary payoff.Using the user-adversary action relationships, we have

p=XT, =X/$t (4)

If p > I , the user is faster than the adversary. If p = 1,the user and the adversary act at the same. In the worstcase, if p < 1, the user acts slower (or has less data). Thedesire to evaluate the effort and risk are shown below

EFFORT Teffort=p/ (I -p)

RISK:

(5)

EX: p = 2, the user is faster 4 T = - 2Hence adversary BLOCKED

p = 1/2, the user is slower 4 T = IHence adversary ACTS

2Revent = p /(l -p) (6)

EX: p = 2, the user is faster 4 R=- 4Hence NEGATIVE EFFECT

p = 1/2, the user is slower 4 R=2Hence POSTIVE EFFECT

Hence the with p > 1, the user is afforded informationfaster which allows the DM cycle to be reduced andcreates a negative effect to adversary action.

4.4 Multiple Operators

Let the number of users be n and number ofadversaries m each observing the situation. Let thenumber of user sensors be N (i.e. UAVs) and M sensorsfor adversaries, then

p = [Nk/n]/[Mp/m] (7)

In this analysis, we are interested to determine thecase for increased use of UAV analysis (N) that affordsmultiple users (n) to act over a distributed set of threats(M) associated with threat actors (m). For example if,

N > I - the user has more observer UAVs4 resulting in a faster decision cycle

n < I - the user has fewer actors4 resulting in efficient DM

m > 1 - the adversary has more actors/ no communication4 the user is less effective in DM

M < I - the user prevents them from observation4 the user-fusion system is more intelligent

The resulting information utilization over Ndistributed sensors for users is effectively

(NTTAv+Nop) X/nP Mgl /m (8)

Hence an fusion of an IFS with an operator increasesthe ability to make event detections in a timely manner.

5 System Performance EvaluationThe goal of any multisensor system intelligent

analysis is to have a fusion gain. The information fusiongain can be assessed as Measures of Effectiveness (MOE)or Measures of Performance (MOP). MOE/Ps can bedetermined from the system analysis. MOPS includethroughput, time, and PD. MOEs include force protection,reduction in casualties, material loss, and eventoccurrence. The goal here is to determineforceprotectiongiven throughput, time, and PD performance.

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Performance metrics include throughput andtimeliness. Throughput can be determined as a theaverage rate, peak rate, and variability of the system todeliver information to the user. The average rate X(events processed / time) is the average load provided by a

source (sensor). The average rate expresses the flow thatcan be sustained by the sensor over an extended period oftime. The peak rate tells the network what type of surgetraffic must be coped with, either by dedicating data-ratecapacity or by allocating sufficient buffer space to smoothout surges. Variability is measured as the throughput peakrate or source burstiness and is an indication of the extentto which statistical multiplexing can increase efficiency.

Delay (or latency) can be measured with time toassessment or delay variation. Given a transfer delay of a

network to move data from a source to a destination and a

delay variation, we are also interested in the ability tomake quick decisions. For this analysis, we are concemedwith the proactive anticipation time:

Anticipation time = [X -_R ]t9

If we utilize the sensor/user throughput [PD =

f(resolution, range)] and timeliness (X, N), we can

determine the MOEs of increased effort (reaction time),reduced risk to threats, distributed detection (PD / area / s),and force protection. Assuming sensor performance, (X =PD / r 2), force protection is:

FP risk = _P

(N[Pp/r 21]In 2(M [ Pr(event) / Area ]/m - N[PD/r2]/n (1)

6 Scenario SimulationFor the homeland protection we are interested in a

force protection environment. We envision thedeployment of multiple UAVs with EO/IR cameras thatare used to detect, ID, and track targets of interest. We arealso interested in the detection of dismounts (i.e. driversexciting their car) which requires RFID and audio IDcheckpoint detection. Figure 11 shows the traffic anddismounts over a 22K x 27K ft area. Assumptions for thebase protection perimeter defense are: events consist ofvehicles with people nearby, responsiveness updates with50% solution, electric high persistence UAVs (solarpowered, 10k ft, 80mph, EO/IR, 1 ft AGR), and wirelesstelemetry for ground processing. We model 30 UAVS,each with an IFS with event detection arrival rate as X, andwith the 50 adversary vehicles each with one sensor, one

threat direction leader, and a movement rate as p.

Assuming orthogonal azimuth detection, we determine thePD as a function of the UAV and vehicle movement. Sincewe utilize N UAVs, we effectively decrease our responsetime and can act quicker for event detection. Figure 11shows the deployed UAVs and the IFS timeless for two

simulations (A) UAVs with EO/IR and RFID tags andAudio-ID from random patrols and (B) random patrols.

Figure 11. Detecting a dismount.

Using the analysis, p = 1.782 and FP Risk = - 0.3418with a proactive anticipation time of 89 seconds to preventaction (determined as the distance to move toward theprotection zones). If PD increases, then the proactiveanticipation time for action would increase.

7 ConclusionsThis paper evaluated a proactive sensor fusion

strategy toward successful anticipation of novel threats.The proactive positioning strategy for force protectiondemonstrated a performance multiplier for urbanoperations. The performance measures of throughput andanticipation time were shown utilizing a sensor fusionstrategy ofEO/IR images, RFID tags, and audio detectionsfrom patrol units. We introduced the measures ofeffectiveness of risk mitigation and increased effort ofadversarial decision making.

8 References[1] Blasch, E., M. Pribilski, et al, "Fusion Metrics for Dynamic

Situation Analysis," Proc SPIE 5429, Aug 2004.[2] Blasch, E. and S. Plano, "Level 5: User refinement to aid

the Fusion Process," Proc SPIE 5099, April 2003.[3] Julier, S. et. al. "Chapter 6: Urban Terrain Modeling for

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[5] Mann, S., "The 'wearable face-recognizer."' WearableComputer-Mediated Reality: WearCam as a wearableface-recognizer, & other apps. for the disabled. 2 February 1996.

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[8] Endsley, M, B. Bolte, & D. Jones, Design for SituationAwareness, Taylor and Francis, 2003.

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[11] Hall, D. L., Mathematical Techniques in MultisensorFusion, Chapter 7. Artech House, Norwood, MA,1992.

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