Detection and Classification Algorithms for Multi-modal...

transcript

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

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

Difficult ProblemVariety of Clutter

&Targets

Variety of Soils&

Environments

Man MadeObjects

Similar toMines

Mines ofDifferent

Sizes,Shapes andMaterials

DryConsistent

WetInconsistent

Sites6

UncertaintyUncertainty

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

generating arc

ε1, µ1, σ1

ε2, µ2, σ2

axis of rotation

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

Optimal classifierMatched filter processor

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

Subspace DetectorBaseline Energy

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1Pfa

Subspace - Sensor/Operator 1Subspace - Sensor/Operator 2Baseline Energy - Sensor/Operator 1Baseline Energy - Sensor/Operator 2

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

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)

( / , ) ( / )( )

( / , ) ( / )

f H f H d

f H f H dΛ = ∫

∫r Θ Θ Θ

rr Ω Ω Ω

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

Multi-modal FusionMulti-modal Fusion

Sensor ResponsesSensor Responses

0 2 4 6 8 10 12 14 16 18 200

0.25Simulated histogram of magni tude of EMI responses

Response

ClutterMine

0 2 4 6 8 10 12 14 16 18 200

0.35Simulated histogram of magnitude of GPR responses

Response

ClutterMine

0.5 1 1.5 2 2.5 3 3.5 40.5

4F1A4 EMI System Cal Lane Data

Response

ClutterMines

0 1 2 3 4 5 6 7 8 90

9HSTAMIDS GPR System Cal Lane Data

Response

e ClutterMines

Sensor Data from Field TrialsSensor Data from Field Trials

0.5 1 1.5 2 2.5 3 3.5 40

9EMI and GPR Cal Lane Data

EMI Response

ClutterMines

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

1ROC Cal Lane Performance

EMIGPRFusion

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

1ROC Blind Grid Performance

EMIGPRFusion

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1Pfa

GEM3 Discrimination AlgorithmF1A4 Energy DetectorWichmann Radar PrescreenerGEM/Wichmann FusionF1A4/Wichmann Fusion

Blind Fusion – Various SystemsBlind Fusion – Various Systems

Multi-modal Iterative Adaptive Processing

Multi-Modal ProcessingMulti-Modal Processing1 1

: , 1 , ( )

: , 1 , ( ), data from tw o sensors, ,

( / , ) ( / )( )

( / , ) ( / )

[ , , ]ˆ ˆ( ), ( )

( / , ) ( / )

( / , ) (

H i Mx y

f H f H d

= + = =

∫∫

∫∫ ∫

r s n s v Θ

r s n s w Θr r

r Θ Θ Θr

r Ω Ω Ω

r Θ Θ Θ

1 11 1

/ ) ( / )

( ) ( / , ) ( / )

H f H d d

p f H f H dθ

∑ ∫

r Θ Θ Θ

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

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

*Sources of uncertainty

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

*Sources of uncertainty

1 , 1 ,1 1

1 11 2

( / ) ( )[ ( / , ) ( )]

AP, AT

x i x j ji j

f H p f t H p tθ

θ θθ

θ θ= =

∑ ∑r r( )

itN θ

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)

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10.1

EMI BaselineEMI LR full uncertaintyEMI LR w/ metal sizeEMI LR w/ metal size and ap/at

Multi-Modal Results: EMIMulti-Modal Results: EMI

1 2[ , ]θ θ

Multi-Modal Results: FusionMulti-Modal Results: Fusion

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

EMI LR full uncertaintyEMI LR w/ metal size and ap/atGPR BaselineEMI/GPR raw fusionEMI postproc/GPR fusion

Multi-modal adaptive fusionMulti-modal adaptive fusion

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

EMI BaselineEMI LR full uncertaintyEMI LR w/ metal sizeEMI LR w/ metal size and ap/atGPR BaselineEMI/GPR raw fusionEMI postproc/GPR fusion

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

Detection and Classification Algorithms for Multi-modal...

Documents