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L. M. Collins, Duke University
Detection and Classification Algorithms for Multi-modal
Inverse Problems
Detection and Classification Algorithms for Multi-modal
Inverse Problems
Leslie M. CollinsElectrical and Computer Engineering
Duke University
Leslie M. CollinsElectrical and Computer Engineering
Duke University
L. M. Collins, Duke University
OverviewOverview
• Background: successes from previous MURI support: false alarm reduction– Physics-based signal processing– Adaptive processing
• Overview of proposed approach– Sensor Fusion– Adaptive multi-modality Bayesian processors
• Preliminary results– Sensor Fusion– Simulated multi-modality processing
• Future Work
• Background: successes from previous MURI support: false alarm reduction– Physics-based signal processing– Adaptive processing
• Overview of proposed approach– Sensor Fusion– Adaptive multi-modality Bayesian processors
• Preliminary results– Sensor Fusion– Simulated multi-modality processing
• Future Work
L. M. Collins, Duke University
Difficult ProblemVariety of Clutter
&Targets
Variety of Soils&
Environments
Man MadeObjects
Similar toMines
Mines ofDifferent
Sizes,Shapes andMaterials
DryConsistent
Sites
WetInconsistent
Sites6
UncertaintyUncertainty
L. M. Collins, Duke University
Combining Phenomenological Models and Bayesian Signal Processing to Improve Object Discrimination
Using EMI Field Data - Approach
Combining Phenomenological Models and Bayesian Signal Processing to Improve Object Discrimination
Using EMI Field Data - Approach
• EMI signatures change as a function of unknowntarget/sensor orientation
• Phenomenological model (Carin et al.) utilized to train a Bayesian algorithm
• Field data collected at arbitrary target/sensor locations from 4 objects
• EMI signatures change as a function of unknowntarget/sensor orientation
• Phenomenological model (Carin et al.) utilized to train a Bayesian algorithm
• Field data collected at arbitrary target/sensor locations from 4 objects
t
z
generating arc
ε1, µ1, σ1
ε2, µ2, σ2
y
x
φ
axis of rotation
L. M. Collins, Duke University
Combining Phenomenological Models and Bayesian Signal Processing to Improve Object Discrimination
Using EMI Field Data - Results
Combining Phenomenological Models and Bayesian Signal Processing to Improve Object Discrimination
Using EMI Field Data - Results
• Performance of Bayesian processor which integrates across uncertainty compared to matched processor that was matched to mean target/sensor location
• On average, 60% improvement in object discrimination
• Performance of Bayesian processor which integrates across uncertainty compared to matched processor that was matched to mean target/sensor location
• On average, 60% improvement in object discrimination
1 2 3 4TARGET NUMBER
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OB
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ILIT
YO
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CTC
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Optimal classifierMatched filter processor
L. M. Collins, Duke University
Adaptive Statistical Signal Processing for Frequency-Domain EMI
Adaptive Statistical Signal Processing for Frequency-Domain EMI
• Preliminary work (left panel) suggested substantial performance gains could be obtained using an adaptive subspace algorithm in a blind field test
• When the algorithm was reapplied (right panel) to data recollected byGeophex in a separate blind field test using two sensors and two operators, similar performance gains were obtained, providing anindication of the robustness of the algorithm
• Preliminary work (left panel) suggested substantial performance gains could be obtained using an adaptive subspace algorithm in a blind field test
• When the algorithm was reapplied (right panel) to data recollected byGeophex in a separate blind field test using two sensors and two operators, similar performance gains were obtained, providing anindication of the robustness of the algorithm
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1Probability of False Alarm
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Pro
babi
lity
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etec
tion
Subspace DetectorBaseline Energy
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1Pfa
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Subspace - Sensor/Operator 1Subspace - Sensor/Operator 2Baseline Energy - Sensor/Operator 1Baseline Energy - Sensor/Operator 2
L. M. Collins, Duke University
Lessons LearnedLessons Learned
• Field data extremely variable – difficult to simulate, noise not Gaussian, test data often not totally consistent with training data
• Physics-based and adaptive processing improves performance for individual sensors
• Little joint optimization or cooperative processing performed – scarcity of multi-sensor or co-located data
• Sensor fusion effective, primarily implemented at “decision level” or “algorithm level”
• Fusion of multiple algorithms operating on same sensor often effective as well
• Field data extremely variable – difficult to simulate, noise not Gaussian, test data often not totally consistent with training data
• Physics-based and adaptive processing improves performance for individual sensors
• Little joint optimization or cooperative processing performed – scarcity of multi-sensor or co-located data
• Sensor fusion effective, primarily implemented at “decision level” or “algorithm level”
• Fusion of multiple algorithms operating on same sensor often effective as well
L. M. Collins, Duke University
Multi-Modal Adaptive Bayesian Processing
Multi-Modal Adaptive Bayesian Processing
• Two modes of adaptation– Statistical parameters tracked and updated (e.g.
covariance matrix)– Priors on uncertain parameters modified based
on context (e.g. size, depth of radar response indicates an anti-tank mine, EMI library modified accordingly)
• Two modes of adaptation– Statistical parameters tracked and updated (e.g.
covariance matrix)– Priors on uncertain parameters modified based
on context (e.g. size, depth of radar response indicates an anti-tank mine, EMI library modified accordingly)
1 1
0 0
( / , ) ( / )( )
( / , ) ( / )
f H f H d
f H f H dΛ = ∫
∫r Θ Θ Θ
rr Ω Ω Ω
L. M. Collins, Duke University
Proposed WorkProposed Work
• Iterative multi-modal adaptive procedure developed and tested on Geophex GEM-3 EMI sensor, Wichmann/NIITEK GPR, Quantum Magnetics QR sensors– Modify underlying statistical models– Modify operating parameters of a suite of sensors– Adaptive beamforming for sensor arrays
• Preliminary test via simulations with existing phenomenological models
• Proof of concept using data collected in the field
• Iterative multi-modal adaptive procedure developed and tested on Geophex GEM-3 EMI sensor, Wichmann/NIITEK GPR, Quantum Magnetics QR sensors– Modify underlying statistical models– Modify operating parameters of a suite of sensors– Adaptive beamforming for sensor arrays
• Preliminary test via simulations with existing phenomenological models
• Proof of concept using data collected in the field
L. M. Collins, Duke University
Multi-modal FusionMulti-modal Fusion
L. M. Collins, Duke University
Sensor ResponsesSensor Responses
0 2 4 6 8 10 12 14 16 18 200
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0.25Simulated histogram of magni tude of EMI responses
P(r
espo
nse)
Response
ClutterMine
0 2 4 6 8 10 12 14 16 18 200
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0.35Simulated histogram of magnitude of GPR responses
P(re
spon
se)
Response
ClutterMine
L. M. Collins, Duke University
0.5 1 1.5 2 2.5 3 3.5 40.5
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4F1A4 EMI System Cal Lane Data
Response
Res
pons
e
ClutterMines
0 1 2 3 4 5 6 7 8 90
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8
9HSTAMIDS GPR System Cal Lane Data
Response
Res
pons
e ClutterMines
Sensor Data from Field TrialsSensor Data from Field Trials
0.5 1 1.5 2 2.5 3 3.5 40
1
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8
9EMI and GPR Cal Lane Data
EMI Response
GPR
Res
pons
e
ClutterMines
L. M. Collins, Duke University
ROC Performance - CalROC Performance - Cal
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
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1ROC Cal Lane Performance
Pfa
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EMIGPRFusion
L. M. Collins, Duke University
ROC Performance - BlindROC Performance - Blind
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
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1ROC Blind Grid Performance
Pfa
Pd
EMIGPRFusion
L. M. Collins, Duke University
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1Pfa
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1P
d
GEM3 Discrimination AlgorithmF1A4 Energy DetectorWichmann Radar PrescreenerGEM/Wichmann FusionF1A4/Wichmann Fusion
Blind Fusion – Various SystemsBlind Fusion – Various Systems
L. M. Collins, Duke University
Multi-modal Iterative Adaptive Processing
Multi-modal Iterative Adaptive Processing
L. M. Collins, Duke University
Multi-Modal ProcessingMulti-Modal Processing1 1
10 0
0
1 1
0 0
1 2
1 2
1 1
1
: , 1 , ( )
: , 1 , ( ), data from tw o sensors, ,
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L. M. Collins, Duke University
Multi-Modal Processing for Landmine Detection
Multi-Modal Processing for Landmine Detection
• Prior work suggests adaptively pruning EMI library using signature magnitude improved processor performance: LM vs HM
• Multi-modality processing – suggests adaptively pruning EMI library using GPR
magnitude: AP vs AT– suggests adaptively pruning GPR library using EMI
discrimination algorithms: mine type– Etc.. (depth, soil moisture)
• Sensor fusion at data level or decision level
• Prior work suggests adaptively pruning EMI library using signature magnitude improved processor performance: LM vs HM
• Multi-modality processing – suggests adaptively pruning EMI library using GPR
magnitude: AP vs AT– suggests adaptively pruning GPR library using EMI
discrimination algorithms: mine type– Etc.. (depth, soil moisture)
• Sensor fusion at data level or decision level
L. M. Collins, Duke University
EMI Signature LibraryEMI Signature LibraryResponse Library
LM HMSig 1 Sig 1Sig 2 Sig 2Sig 3 Sig 3
Sig M-1Sig M
Sig N-1Sig N
APAP
AT
AT
*Sources of uncertainty
L. M. Collins, Duke University
EMI Signature LibraryEMI Signature LibraryResponse Library
LM HMSig 1 Sig 1Sig 2 Sig 2Sig 3 Sig 3
Sig M-1Sig M
Sig N-1Sig N
APAP
AT
AT
*Sources of uncertainty
1θ
2θ
( )21
1 , 1 ,1 1
1 11 2
( / ) ( )[ ( / , ) ( )]
AP, AT
t i
i i
N
x i x j ji j
f H p f t H p tθ
θ θθ
θ θ= =
=
= =
∑ ∑r r( )
1
itN θ
L. M. Collins, Duke University
Multi-modal SimulationsMulti-modal Simulations
• EMI:– 4 subclasses within landmines (AP/AT,
LM/HM)– 4 subclasses within clutter (0, L, M, H)
• GPR– 2 subclasses within landmines (AP/AT)– 2 subclasses within clutter (Y/N)
• EMI:– 4 subclasses within landmines (AP/AT,
LM/HM)– 4 subclasses within clutter (0, L, M, H)
• GPR– 2 subclasses within landmines (AP/AT)– 2 subclasses within clutter (Y/N)
L. M. Collins, Duke University
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10.1
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EMI BaselineEMI LR full uncertaintyEMI LR w/ metal sizeEMI LR w/ metal size and ap/at
Multi-Modal Results: EMIMulti-Modal Results: EMI
2θ
1 2[ , ]θ θ
L. M. Collins, Duke University
Multi-Modal Results: FusionMulti-Modal Results: Fusion
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EMI LR full uncertaintyEMI LR w/ metal size and ap/atGPR BaselineEMI/GPR raw fusionEMI postproc/GPR fusion
L. M. Collins, Duke University
Multi-modal adaptive fusionMulti-modal adaptive fusion
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EMI BaselineEMI LR full uncertaintyEMI LR w/ metal sizeEMI LR w/ metal size and ap/atGPR BaselineEMI/GPR raw fusionEMI postproc/GPR fusion
L. M. Collins, Duke University
Conclusions/Future WorkConclusions/Future Work
• Adaptive multi-modality processing holds promise for improved performance
• Co-located data required to perform sensor fusion or multi-modality processing.
• Further theoretical work and simulations to quantify performance gain
• Tests on data collected during field trials
• Adaptive multi-modality processing holds promise for improved performance
• Co-located data required to perform sensor fusion or multi-modality processing.
• Further theoretical work and simulations to quantify performance gain
• Tests on data collected during field trials