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Networked ATR Systems Design Considerations: System Performance
and Resource Constraints
Joseph A. O’Sullivan Electronic Systems & Signals Research
LaboratoryDept. Electrical & Systems Engineering
Washington [email protected]
http://essrl.wustl.edu/~jao
Presented June 5, 2003 at ONR Command, Control and Combat Systems Gathering.
Michael D. DeVoreDepartment of Systems and Information
EngineeringUniversity of Virginia
[email protected]://people.virginia.edu/~md9c
Alan Van NevelNAVAIR
China Lake, CA
J. A. O’Sullivan, M. D. DeVore, A. Van Nevel ONR Meeting 6/5/2003Networked ATR Systems Design
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CollaboratorsCollaborators
Donald L. SnyderDaniel R. FuhrmannRobert PlessNatalia A. Schmid (to WVA)Brandon Westover • MD/PhD in Neuroscience• Charlie Anderson• David Van Essen
Joseph A. O’Sullivan, WUSTLMichael D. DeVore, UVA
Supported by ONRFaculty
Others
StudentsLee MontagninoAndrew Li
J. A. O’Sullivan, M. D. DeVore, A. Van Nevel ONR Meeting 6/5/2003Networked ATR Systems Design
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OutlineOutline• Motivation
- Navy Problems- Dynamic Network Implementations for ATR
• Rate-Recognition Theory- Coding Analogy- Theorems
• Implementation Considerations- Successive Refinement- Communication-Computation
• Conclusions
J. A. O’Sullivan, M. D. DeVore, A. Van Nevel ONR Meeting 6/5/2003Networked ATR Systems Design
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OutlineOutline• Motivation
- Navy Problems- Dynamic Network Implementations for ATR
• Rate-Recognition Theory- Coding Analogy- Theorems
• Implementation Considerations- Successive Refinement- Communication-Computation
• Conclusions
G ENERARE U UTF RUM
CNO SSG XX FORCENetVision
Enhanced Capabilities•Shared battlefield awareness•Speed of decision making•Effects-based operations•Dispersed force protection•Mission flexibility•Global reach back•Sustainability
Networks •Fully-netted force•Processing and collaboration•Real-time shared knowledge
Multi-Tiered Sensor Field•Scalable - space to sea bed•National/Theater/Organic assets•The Warrior
““The architecture for a The architecture for a fully netted maritime forcefully netted maritime force””
Theater Ballistic
Missile Defense
Theater Air Defense
Command & Control
Naval Fires
Undersea Warfare
Sustainment
J. A. O’Sullivan, M. D. DeVore, A. Van Nevel ONR Meeting 6/5/2003Networked ATR Systems Design
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ATR System ImplementationATR System Implementation
System model accounts for:• Recognition Algorithm• Number of bits to
represent an image• Network throughput• Processor cycles per
instruction• Size of processor local
memory• etc.
Goal: Near optimal use of resources in a dynamic environment
• System operates within constraints- Accuracy, bandwidth, computational capability, throughput
• Demands and resources may change during an engagement- Damage, jamming, new capabilities, preemption, budgeting, etc.
Sensor Data Link
Weapon Link
Shooter Link
•Recce Imagery (SAR, IR, Visible)•Intelligent Bandwidth Compression
Ground Recce/Intel Station Strike Planning System
•Wide Area Cueing (ATC)
•Select Target
Targeting Info Link•Link Target data to TOC(type, location, motion,…)
•Strike Planning•Weapon Selection
•ATR•Reference Library, Aimpoint
Terminal ATR
•Strike plan•Target location•ATR parameters
Network Centric Warfare:The Sensor to Shooter Problem
J. A. O’Sullivan, M. D. DeVore, A. Van Nevel ONR Meeting 6/5/2003Networked ATR Systems Design
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Navy Need/ImpactNavy Need/Impact• Need
– Robust networked processing for automatic target recognition (ATR)
– Ability to leverage real-time sharing of data as envisioned in FORCEnet
» Rapidly evolving system architecture» Evolving time constraints» Evolving set of targets
• Impact– Successively refinable ATR: near optimal performance
subject to dynamic time constraints– ATR system performance subject to dynamic resource
constraints dynamically reconfigurable ATR– Development of system design guidelines based on known
processor/timeline/bandwidth limitations– Extract information content from imagery, maximizing
bandwidth usage
J. A. O’Sullivan, M. D. DeVore, A. Van Nevel ONR Meeting 6/5/2003Networked ATR Systems Design
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ATR Performance and ComplexityATR Performance and ComplexityComparison in terms of:• Performance achievable at a given complexity• Complexity required to achieve a given performanceInformation Theory Basis: Rate-Distortion Theory
Rate-Recognition Theory
• Theoretical curves• Operational curves• Actual performance
Model errorsVarying clutter, backgroundNonideal implementations
J. A. O’Sullivan, M. D. DeVore, A. Van Nevel ONR Meeting 6/5/2003Networked ATR Systems Design
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OutlineOutline• Motivation
- Navy Problems- Network Implementations for ATR
• Rate-Recognition Theory- Coding Analogy- Theorems
• Implementation Considerations
- Successive Refinement- Communication-Computation
• Conclusions
J. A. O’Sullivan, M. D. DeVore, A. Van Nevel ONR Meeting 6/5/2003Networked ATR Systems Design
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Object Recognition AbstractionObject Recognition Abstraction
• Objects as Random Vectors • Recognition as M-ary Hypothesis Testing• Analogy to Random Coding Problems
p(xn) p(xn) p(xn) p(yn |xn)Selectone
Model Memory
…Xn(1) Xn(M)Xn(2) Yn
Xn(h)
h
J. A. O’Sullivan, M. D. DeVore, A. Van Nevel ONR Meeting 6/5/2003Networked ATR Systems Design
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Models for Objects: Physical SignaturesModels for Objects: Physical Signatures• Signatures as realizations of random processes• Joint models for multiple measurements
– Model for underlying physical signature– Models for measurements used to derive templates p(Xn)
M independent templates– Models for measurements of candidate
signatures p(Yn)– Joint distribution given identical
physical signature p(Xn,Yn) with marginal distributions p(Xn) and p(Yn)
– This talk: pairwise i.i.d.• Measure of Size n
– Example: increasing image resolution
J. A. O’Sullivan, M. D. DeVore, A. Van Nevel ONR Meeting 6/5/2003Networked ATR Systems Design
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Example: Radar ATR Example: Radar ATR • Publicly available SAR images from MSTAR program;
over 15,000 images in data set• Model measurements as realizations of stochastic
processes• Estimate model parameters for training
2S12S1 T62T62 BTR 60BTR 60 D7D7 ZIL 131ZIL 131 ZSU 23/4ZSU 23/4
J. A. O’Sullivan, M. D. DeVore, A. Van Nevel ONR Meeting 6/5/2003Networked ATR Systems Design
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Preliminary ResultsPreliminary Results• M-ary hypothesis testing
– results rely on two information rate functions– Chernoff bounds– Rate for minimum probability of error is Chernoff information
(two rate functions cross at zero threshold)
• Capacity results: guaranteed error rate, exponential growth in number of hypotheses– Signatures analogous to random codewords for channel
coding– Recognition problem analogous to communication with
random codebook– Channel reliability function gives growth rate
J. A. O’Sullivan, M. D. DeVore, A. Van Nevel ONR Meeting 6/5/2003Networked ATR Systems Design
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Model for Generic Object Model for Generic Object Recognition SystemRecognition System
p(xn) p(xn) p(xn)
φ
p(yn |xn)Selectone…
ff ff ff…
Xn(1) Xn(M)Xn(2) Yn
Xn(h)
hµm(1) m(2) m(M)
J. A. O’Sullivan, M. D. DeVore, A. Van Nevel ONR Meeting 6/5/2003Networked ATR Systems Design
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Generic ModelGeneric Model
• Objects or Patterns Learned Over Time– Long-term memory– Finite (compressed) representation
• Observation Depends on Object– Randomly selected object– Random observation model– Visual image, sensed pattern, biometrics
• Finite (compressed) Representation of Data
J. A. O’Sullivan, M. D. DeVore, A. Van Nevel ONR Meeting 6/5/2003Networked ATR Systems Design
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Rate Region for Pattern RecognitionRate Region for Pattern Recognition• Let Xn(j) be i.i.d. with i.i.d. elements;M= 2nRc
• Fix compression functions f and φ• Compression ranges 2nRx and 2nRy
• Finite (compressed) representation of data• A rate triple (Rc,Rx,Ry) is achievable if there is
a sequence of such codes whose probability of error goes to zero as n goes to infinity.
• Define mutual information between two random variables U and V to be
( )∑∑=u v
vpupvupvupVUI )()(),(ln),();(
J. A. O’Sullivan, M. D. DeVore, A. Van Nevel ONR Meeting 6/5/2003Networked ATR Systems Design
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Rate Region for Pattern Rate Region for Pattern Recognition: TheoremRecognition: Theorem
• Suppose there exist finite sets U and V and distributions p(u|x) and p(v|y). Then all rate triples satisfying
are achievable• Conversely, if a rate triple is achievable,
then there exist finite sets U and V and distributions p(u|x) and p(v|y) satisfying
);(),;(),;( VUIRYVIRXUIR cyx <>>
);(),;(),;( VUIRYVIRXUIR cyx ≤≥≥
J. A. O’Sullivan, M. D. DeVore, A. Van Nevel ONR Meeting 6/5/2003Networked ATR Systems Design
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Model for Generic Object Model for Generic Object Recognition SystemRecognition System
p(xn) p(xn) p(xn)
φ
p(yn |xn)Selectone…
ff ff ff…
Xn(1) Xn(M)Xn(2) Yn
Xn(h)
hµm(1) m(2) m(M)
Un(1) Un(2) … Un(M) Vn
J. A. O’Sullivan, M. D. DeVore, A. Van Nevel ONR Meeting 6/5/2003Networked ATR Systems Design
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Comments on Comments on RateRate––Recognition TheoremRecognition Theorem
• Single-letter characterization• Not surprising … (plausible rate bounds)
• Surprising … (distributed information theory)
• Memory: databases, brains• Image representation: digital, neural firing
patterns• Given representations, find limits• Given recognition rate, optimize representations
);(),;(),;( VUIRYVIRXUIR cyx ≤≥≥
J. A. O’Sullivan, M. D. DeVore, A. Van Nevel ONR Meeting 6/5/2003Networked ATR Systems Design
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OutlineOutline• Motivation
- Navy Problems- Dynamic Network Implementations for ATR
• Rate-Recognition Theory- Coding Analogy- Theorems
• Implementation Considerations- Successive Refinement- Communication-Computation
• Conclusions
J. A. O’Sullivan, M. D. DeVore, A. Van Nevel ONR Meeting 6/5/2003Networked ATR Systems Design
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ModelModel--Based RecognitionBased Recognition
= argmax p(r | a, θ)a,θ
aθ
^^
Wherer is an observation vectora is a target classθ is a target pose
For fixed a, function p constitutes a target model- Generally estimated from training data- Often of a complexity-restricted class- May be a standard form parameterized by a function
Maximum-likelihood from statistical models
Target Target ClassifierClassifier
ââ=T72=T72
J. A. O’Sullivan, M. D. DeVore, A. Van Nevel ONR Meeting 6/5/2003Networked ATR Systems Design
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Likelihood ApproximationsLikelihood Approximations• Choose a sequence of approximations p1, p2, …
with pn+1 a better approximation than pn
Pr[error | pn+1] ≤ Pr[error | pn]
• Let C(pn) be a measure of average resource consumption or complexity when approximation pn is employed
• Choose sequence so that better approximations involve higher complexity
C(pn+1) ≥ C(pn)• Standard Approach: Static implementation by
selecting n to satisfy constraints on Pr[error | pn] and C(pn)
J. A. O’Sullivan, M. D. DeVore, A. Van Nevel ONR Meeting 6/5/2003Networked ATR Systems Design
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Example: Radar ATR Example: Radar ATR Theory and PerformanceTheory and Performance
• Publicly available SAR images from MSTAR program; over 15,000 images in data set
• Compare performance and database complexity for a variety of data models
2S12S1 T62T62 BTR 60BTR 60 D7D7 ZIL 131ZIL 131 ZSU 23/4ZSU 23/4
J. A. O’Sullivan, M. D. DeVore, A. Van Nevel ONR Meeting 6/5/2003Networked ATR Systems Design
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Recognition Performance Recognition Performance
•Model correctness: validity of statistical likelihood•Model segmentation: parts of data modeled
•Approximation complexity: number of poses
BMP2 Variance Image at 6 Sizes
Fine model of a T72 tank (1/7 relative scale),72 templates totaling 1.1M floats
Coarse model of aT62 tank, 1 template with 16K floats
J. A. O’Sullivan, M. D. DeVore, A. Van Nevel ONR Meeting 6/5/2003Networked ATR Systems Design
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ModelModel--Based RecognitionBased Recognition
FunctionFunctionEstimationEstimation
LL((rr||aa,,θθ)) InferenceInference
Scene and SensorScene and SensorPhysicsPhysics
Training DataTraining Data
ProcessingProcessing
ââ=T72=T720 50 100 150 200 250 300 350 400
7.4
7.6
7.8
8
8.2
8.4
8.6
8.8x 104
Azimuth (degrees)
Raw HRR Raw HRR DataData
SAR ImageSAR Image LogLog--likelihoodlikelihoodfunctionfunction
J. A. O’Sullivan, M. D. DeVore, A. Van Nevel ONR Meeting 6/5/2003Networked ATR Systems Design
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Example ResultsExample Results
•240 variations per model families•10 model families (6 reported)
J. A. O’Sullivan, M. D. DeVore, A. Van Nevel ONR Meeting 6/5/2003Networked ATR Systems Design
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PerformancePerformance--Complexity LegendComplexity Legend
Forty combinations of number of piecewise constant intervals and training window width
J. A. O’Sullivan, M. D. DeVore, A. Van Nevel ONR Meeting 6/5/2003Networked ATR Systems Design
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Example: Approximating LikelihoodsExample: Approximating Likelihoods
••••••
• Model the SAR image of a bulldozer as a function of azimuthri ~ CN(0, σi
2(a,θ) )
• Likelihood function depends on parameter function σi2
• Sequence of piecewise constant approximations
Increasing AngularResolution
↓
Increasing azimuth →
σ 2(D7,θ)
J. A. O’Sullivan, M. D. DeVore, A. Van Nevel ONR Meeting 6/5/2003Networked ATR Systems Design
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Dynamic Dynamic ReconfigurabilityReconfigurability• Seek algorithms that dynamically adjust to fit
requirements- can’t necessarily determine n ahead of time
• Let ∆C(pn+1) be the additional resources consumed using pn+1 assuming problem with pn already solved
• Good designs characterized by
C(pn+1) ≈ C(pn) + ∆C(pn+1)
• Produce a sequence of answers (a1,θ1), (a2,θ2), … with increasing accuracy and resource consumption
C'(pn+1) = C(p1) + ∆C(p2) + … + ∆C(pn+1)- stop when resource allocation exhausted
J. A. O’Sullivan, M. D. DeVore, A. Van Nevel ONR Meeting 6/5/2003Networked ATR Systems Design
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Example: Delta Cost FunctionsExample: Delta Cost Functions• Let cost be average number of bits read from
database• Divide azimuth into Nd non-overlapping
intervals of width d
˜ σ d,i2 θk , a( )=
1d
σi2 θ, a( )dθ
2πkNd
− d2
2πkNd
+ d2
∫Approximations d and d/2
are hierarchically related: ( ) ( ) ( )[ ] ,~,~,~
122,2
2,2
12, 22
aaa kikikid dd ++= θσθσθσ
J. A. O’Sullivan, M. D. DeVore, A. Van Nevel ONR Meeting 6/5/2003Networked ATR Systems Design
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Sequence SelectionSequence Selection• Selection of sequence pn drastically affects the
parametric curve Pr[error | pn] vs. C'(pn)• Good designs decrease error rapidly at start of
sequence- useful results even if search is terminated early- can make use of additional resources if available
• Example: Error probability vs. database communication- Design #1: “Leaf Search”Refine sequential 1.4º intervals
- Design #2: “Breadth First”Divide the most likely interval
J. A. O’Sullivan, M. D. DeVore, A. Van Nevel ONR Meeting 6/5/2003Networked ATR Systems Design
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Example: Search AlgorithmExample: Search Algorithm
Error rate vs. bits transmitted from database to processor
• Classification depends on extent of search
• Eventually, search covers all possibilities
• Breadth-first search quickly finds good solutions (a, θ)
• Small overhead present with ordered searches
J. A. O’Sullivan, M. D. DeVore, A. Van Nevel ONR Meeting 6/5/2003Networked ATR Systems Design
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Other Consumption MeasuresOther Consumption Measures• Network bandwidth is one of many types of
resources• Other average rates of resource consumption:
– Elapsed time per classification– CPU cycles per classification– Database (magnetic) storage per model class– Power dissipation
• Changing resource consumption rates due to:– Variation in application requirements– Reallocation of resources to higher priority tasks– Damaged or offline computation elements– Disrupted communication paths– Power considerations
J. A. O’Sullivan, M. D. DeVore, A. Van Nevel ONR Meeting 6/5/2003Networked ATR Systems Design
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ExampleExample
• Pr[error] as function of:- Power consumption (W)- Processing time (s)
• Constant processor clock frequency (500 MHz)
• Variable network utilization
- From 100Mb/s to 1Gb/s
How does resource availability affect classifier accuracy?
J. A. O’Sullivan, M. D. DeVore, A. Van Nevel ONR Meeting 6/5/2003Networked ATR Systems Design
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ExampleExampleHow can a lack of one resource by accommodated?
Network utilization, processing time, and
power consumption to achieve Pr[error] = 3%
Variable processor clock frequency from 500MHz to
700MHz
J. A. O’Sullivan, M. D. DeVore, A. Van Nevel ONR Meeting 6/5/2003Networked ATR Systems Design
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Networked ATR SystemNetworked ATR System
• Local capabilities plus interaction with remote components
• Communicate observation as well as classification- Human in the loop- Remote contribution to classification when available
• Sequence of partial classifications (an,θn)
J. A. O’Sullivan, M. D. DeVore, A. Van Nevel ONR Meeting 6/5/2003Networked ATR Systems Design
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ConclusionsConclusions
•• New Theorems in RateNew Theorems in Rate--Recognition Recognition TheoryTheory
—— Mutual InformationMutual Information——BoundsBounds—— Joint Compression and Channel CodingJoint Compression and Channel Coding
•• Constrained Implementations of ATR Constrained Implementations of ATR ForceNetForceNet
—— Dynamic resource constraintsDynamic resource constraints—— System PerformanceSystem Performance——System ResourceSystem Resource
J. A. O’Sullivan, M. D. DeVore, A. Van Nevel ONR Meeting 6/5/2003Networked ATR Systems Design
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Project Objectives1.1.5.1 Data Compression
1.1.5.1.3 ATR issues from compression
1.3.2 Automated Resource Allocation1.3.2.1 Resource allocation not performed with respect to operational conditions or network status
4.1 Information Integration Fusion4.1.5 Mathematical issues processing large number of sensors and propagating errors
Networked ATR Systems Design Considerations: System Performance and Resource Constraints
Project Description & Technical Approach
The work will develop system implementation guidelines and ATR algorithms which can operate near optimally under dynamic constraints. The system will adapt to evolving system architectures, time constraints, processor availability and communication bandwidth. A fundamental theoretical goal is the extension of rate-distortion theory for communications to a rate-recognition theory for ATR. A component of this approach will use successively refinable models to allow for efficient and flexible processing.
Project Payoff/Impact on Naval Needs
• Provide flexibility and optimal computing architecture within the Expeditionary Sensor Grid and ForceNet• Enable collaborative and distributed ATR processing in a dynamic environment• Quantifiable ATR performance metrics and probabilities• Robust ATR capabilities
Project Start/Milestones/Funding
J. A. O’Sullivan, M. D. DeVore, A. Van Nevel ONR Meeting 6/5/2003Networked ATR Systems Design
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J. A. O’Sullivan, M. D. DeVore, A. Van Nevel ONR Meeting 6/5/2003Networked ATR Systems Design
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ConclusionsConclusionsNavy/Marine Corps ImpactNavy/Marine Corps Impact
•• Successively Successively refinablerefinable ATR: near optimal performance ATR: near optimal performance
subject to dynamic time constraints subject to dynamic time constraints
•• ATR System performance subject to dynamic resource ATR System performance subject to dynamic resource constraints constraints dynamically reconfigurable ATRdynamically reconfigurable ATR
•• Development of system design guidelines based on Development of system design guidelines based on known processor/timeline/bandwidth limitationsknown processor/timeline/bandwidth limitations
•• Extract information content from imagery, Extract information content from imagery, maximize bandwidth usagemaximize bandwidth usage
J. A. O’Sullivan, M. D. DeVore, A. Van Nevel ONR Meeting 6/5/2003Networked ATR Systems Design
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ATR Problem ScopeATR Problem Scope
• Multiple imaging sensors: LADAR, IR, EO, Multispectral, Hyperspectral, SAR
• Network of sensors, processors, databases • Constraints imposed by system architecture, time constraints, and
target model set• Successive refinability to dynamically tradeoff accuracy and
resources (time, bandwidth, computations, etc.)
Model Database
Scene(source)
ImagingSensor Platform
ATR ProcessorInferred Target
and Pose
ImagingSensor Platform Interconnection
Networks
ImagingSensor Platform
J. A. O’Sullivan, M. D. DeVore, A. Van Nevel ONR Meeting 6/5/2003Networked ATR Systems Design
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Point of ViewPoint of View
Navy Applications TheoryMotivation, Implementation Constraints
Theory: abstraction, idealized models and constraintsSystem design guidelines, target
computations (compression, models, templates)
Simulations, performance bounds
J. A. O’Sullivan, M. D. DeVore, A. Van Nevel ONR Meeting 6/5/2003Networked ATR Systems Design
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Model for Generic Object Model for Generic Object Recognition SystemRecognition System
p(xn) p(xn) p(xn)
φ
p(yn |xn)Selectone…
ff ff ff…
Xn(1) Xn(M)Xn(2) Yn
Xn(h)
hµm(1) m(2) m(M)
Un(1) Un(2) … Un(M) Vn
J. A. O’Sullivan, M. D. DeVore, A. Van Nevel ONR Meeting 6/5/2003Networked ATR Systems Design
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Traditional Approaches to ATRTraditional Approaches to ATR• Platform-centric, stovepiped• Subsystems (sensor, communications, data processing)
have independent design and operation• Difficult to prove overall system is operating optimally• Often brittle point solutions
Fixed• System architecture
Sensors, computational hardware, communications,geometry, signal processing blocks (compression, etc.)
• Time constraints• Target model set
J. A. O’Sullivan, M. D. DeVore, A. Van Nevel ONR Meeting 6/5/2003Networked ATR Systems Design
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Single ATR SystemSingle ATR System
Model Database
Scene SensorPlatform
InterconnectionNetwork
ATRProcessor Inferred Target
and Pose
ATR processor solves a sequence of inference problemsMay sport multiple CPUsProblem computations may overlap
Sensor platform collects scene information for processorMay incorporate multiple measurements
Model database represents known objectsMay support multiple resolutions
Network connects sensor, processor, and database
J. A. O’Sullivan, M. D. DeVore, A. Van Nevel ONR Meeting 6/5/2003Networked ATR Systems Design
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Differential EntropyDifferential Entropy
Plot of average differential entropy of r given a=a and θ in [Θ,Θ+∆Θ] as a function of ∆
Networked ATR Systems Design Networked ATR Systems Design Considerations: System Performance Considerations: System Performance
and Resource Constraintsand Resource ConstraintsJ. A. OJ. A. O’’Sullivan, Washington USullivan, Washington U
M. D. M. D. DeVoreDeVore, U. Virginia, U. VirginiaA. Van A. Van NevelNevel, NAVAIR, NAVAIR
J. A. O’Sullivan, M. D. DeVore, A. Van Nevel ONR Meeting 6/5/2003Networked ATR Systems Design
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• Derive statistical models from data, physics, constraintspR|Θ,a(r|θ,a) - conditional data modelpΘ|a(θ|a) – prior probability on orientationp(a) – prior probability on target class
• Recognition: select most likely target• Evaluate performance over test set• Repeat for different models and complexity constraints
Target Target ClassifierClassifier
ââ=T72=T72Given a SAR image r, determine a corresponding target class â∈A and pose θ
Target Recognition ProblemTarget Recognition ProblemStatistical Likelihood ApproachStatistical Likelihood Approach
J. A. O’Sullivan, M. D. DeVore, A. Van Nevel ONR Meeting 6/5/2003Networked ATR Systems Design
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Recognition Performance Recognition Performance
•Model correctness: validity of statistical likelihood•Model segmentation: parts of data modeled
•Approximation complexity: number of poses
BMP2 Variance Image at 6 Sizes
Fine model of a T72 tank (1/7 relative scale),72 templates totaling 1.1M floats
Coarse model of aT62 tank, 1 template with 16K floats
J. A. O’Sullivan, M. D. DeVore, A. Van Nevel ONR Meeting 6/5/2003Networked ATR Systems Design
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Example ResultsExample Results
•240 variations per model family•10 model families (6 reported)