Detection of Obscured Targets: Signal Processingpeople.ee.duke.edu/~lcarin/McClellan MURI Review...

Detection of Obscured Targets: Signal Processing

James McClellan and Waymond R. Scott, Jr. School of Electrical and Computer Engineering

Georgia Institute of TechnologyAtlanta, GA 30332-0250

[email protected]

MURI Review 2-25-04 Scott/McClellan, Georgia Tech 2

Outline

IntroductionMulti-resolution & Multi-modal Signal Processing

Physical Basis for Multimodal Processing/InversionQuadtree Imaging

SP for Three Sensor ExperimentReverse-Time Processing

FocusingImaging

Accomplishments/Plans


Three Sensor ExperimentA three sensor experiment has been developed to investigate the potential for multimodal processing

Electromagnetic Induction (EMI) SensorGround Penetrating Radar (GPR) SensorSeismic Sensor

Multiple Experimental ScenariosBuried LandminesBuried Clutter Objects Target Distribution

Properties

Physical Properties of TargetSensor

Yes

No

No

Mechanical Contrast

NoNoNoSeismic

Yes*YesYesGPR

YesWeakNoEMI

High Conductivity

(Metal)

Low Conductivity(Dielectric)

Permittivity Contrast


Comparison of EMI, GPR and Seismic Response VS-50, 1 cm deep

EMI GPR Seismic

depth

x

ty

0.00

1,000 10,000Frequency (Hz)

RealImag.

-0.02

0.02

0.04


Multi-Sensor Processing

GPR

EMI

Seismic

Imaging

SigProc

Imaging

Features

Features

Features

DecisionProcess

ExploitCorrelation

Training

Detect

Classify

ID


Multi-Sensor Adaptation

GPR

EMI

Seismic

Imaging

SigProc

Imaging

Features

Features

Features

DecisionProcess

ExploitCorrelation & Sensitivity

Feedback

Feedback

Controls

Controls

Controls

Controls

Controls

Controls


Three Sensor ExperimentSensor Adjustments and Features

Adjustable Parameters for all three sensors

Frequency rangeFrequency ResolutionSpatial ResolutionIntegration time/bandwidthHeight above ground

Possible Features for sensorsEMI

Relaxation frequencyRelaxation strengthRelaxation shapeSpatial response

GPRPrimary ReflectionsMultiple ReflectionsDepthSpatial Response

SeismicResonanceReflectionsDispersionSpatial response


Multi-Resolution Processing

GPR

EMI

Seismic

Imaging

SigProc

Imaging

Features

Features

Features

DecisionProcess

ExploitCorrelation

Training

Detect

Classify

ID

Quadtree Imaging@ increasingResolution(Eliminate Areas)

Target Localization@ specific sites

Multi-band Imaging


Outline




FocusingImaging



Multi-Res Quadtree Algorithm

10

Standard BackprojectionSpace-Time Domain CorrelationSignificant amount of computations

Quadtree AlgorithmApproximates Standard BackprojectionConsists of many sub-aperture and sub-image operations:

beamforming over the sensors in sub-apertures with respect to the virtual sensor and sub-images.Significant amount of Time-Domain Interpolation required.

Quadtree BackProjection

Sub-Aperture Formation

(Virtual Sensor)

Image Patch Dividing

(Sub-Patch )

Quadtree BackProjection

1st Stage

2nd Stage

3rd Stage

4th Stage

Space-Time Domain Decomposition Image Patch Dividing and Sub-Aperture Formation (Virtual Sensor)Divide and Conquer Strategy

Computational Complexity O(N2log2N)

Quadtree PruningMultiresolution Imaging

Intermediate Data with Energy Functiond(u,t) ⇒ di(u,t,1,1) ⇒ … ⇒ di(u’,t’,ξ,η) ⇒ … ⇒ di(1,1,ξ,η) ⇒ f(x,y)

Intermediate Stage Data Pruning ⇒⇒⇒⇒ Early Detection


Quadtree Equivalence

Quadtree BackProjection Imaging AlgorithmComputes sub-images with sub-aperturesUltra WideBand (UWB) SAR: FOPEN

Quadtree Tomographic BackProjectionTomographic ImagingMultiresolution Imaging

Quadtree Broadband BeamformerDelay-Sum BeamformerMulti-Angle Multiresolution Beamforming


Outline




FocusingImaging



Three Sensor Experiment

Experimental Scenario #16 Mines> 20 Clutter objectsRelatively uniform distribution

Experimental Scenario #27 Mines> 25 Clutter objectsNon-uniform distribution


EMI Processing

Frequency Domain: 600 Hz to 60 KHz

Extract the break frequency via signal modeling & form an image


Burial Scenario #1

1.8m by 1.8m Scan Region

Rocks (3 and4 cm deep)

Dry Sand(5cm deep)

MINESVS-2.2

(7cm deep)

TS-50(1.5cm deep)

w/ Nail

M-14(0.5cm deep)

VS-50(1cm deep)

EMF-1(1.5 cm deep)

VS-1.6(6.5cm deep)

SeismicSources

Cans (3 and2.5 cm deep)

AssortedMetal Clutter (2 to 4 cm deep)

Shells(4cm deep)

ThreadedRod(3.5cm deep)

Penny(5.5cm deep)

Nails(4cm deep)

Ball Bearing(3.5cm deep)

Shells(5.5cm deep)


Energy Plot (db scale)Break Frequencies in Hz (linear scale)Frequency Range: 300-60,000 HzBurial Scenario-1

Energy Plot Break Frequencies


Burial Scenario #2

1.8m by 1.8m Scan Region

SeismicSourcesMINES

VS-50(1.3cm deep)

VS-2.2(5.4cm deep)

M-14(1cm deep)

TS-50(1.3cm deep)

EMF-1(0.6cm deep)

VS-50(0.5cm deep)

VS-1.6(5.1cm deep)

Rocks(2, 2.2, 2.5,and 1.3cm deep)

Can(2.2cm deep)

AssortedMetalClutter(


“QUADTREE” Burial Scenario-2

Energy Plot Break Frequencies


GPR ProcessingData taken in frequency domain with network analyzer: 500 MHz to 8 GHzImaging

Backprojection does migration2-D, extend to 3-Dωωωω-k algorithmsExtend to Quadtree

Multi-resolution

y

x

2 =

4.5

"A

w = 3mm

2 =

62.

4mil

a

L = 6.75"Antenna Shape


GPR Processing ExampleOriginal Data Cut Ground Reflection

Removed By CorrelationImage is formed by Back Projection Algorithm

VS-50

TS-50

VS-1.6

VS-2.2


Total Energy Imaged(Burial #2 Quadtree)


Energy in Different Depth Slices(Burial #2 Quadtree)


Seismic Sensor

Radar:R.F. Source,Demodulator, andSignal Processsing

Signal Generator

Elastic WaveTransducer

ElasticSurfaceWave

Mine

E.M. Waves

AirSoil

S N S

Wav

egui

deDisplacements


Seismic SensorImage 30 dB Scale


Outline




FocusingImaging



Time Reverse Imaging

Probe the medium containing targets with P sources and measure reflection on N sensors

P sources (p) and N Receivers (q), each source sends a pulse which is received by N sensors

Form the Response Matrix, P(t), ( P x N x T )

Process P(t) in frequency domainOne frequency at a time

• Borcea, Papanicolaou, Tsogka, Berryman, “Imaging and Time Reversal in Random Media,” Inverse Problems, 2002.


Problem Definition


Time Reversal MethodEach source (P) sends a pulse, then scattered waves are received by receivers (N) to build up a response matrixIf e(t) is the transmitted pulse, then the received signal at each receiver is

Frequency Domain


Time Reversal MethodIn Frequency domain, time reversal is equivalent to phase conjugation, hence after one time reversal operation

This signal is linked to the transmitted signal through a phase conjugation and a matrix called Time-Reversal Matrix


Response Matrix Response matrix is given in terms of Green’s function between sources and targets and receiver and targets

M = number of targets, G(y,x,ω) = Green’s functionEigenvalues and eigenvectors of response matrix are related to each target


Time Reversal MatrixSVD of response matrix and time reversal matrix is related by

Time Reversal Matrix can be interpreted as covariance matrix used in standard array processing techniques*

Receivers correspond to sensorsSources correspond to snapshots

* Prada, JASA., Vol. 144, No1, July 2003


SVD of response matrix in Imaging

Determine number of targets (by using significant eigenvalues)Localize by using the eigenvectors

MUSIC-like methods: null vs. the “Noise Subspace”


Near Field DOA & Range Estimation

Time Reversal Matrix can be interpreted as covariance matrix

Images obtained from Time Reversal have poor range resolution.

Formulate new methods for high resolution Range and DOA estimates?

Detect the position of targets using near-field DOA and Range estimatesEstimate target location with “Spot-Forming”


Signal Model For Near Field

n sources located at (Ri,θi)Array of m sensors, with spacing d, with aperture of (m-1)dRange Information is given in terms of reference sensor #1


Time Reversal & Near Field Model


Green’s Vector


Method for estimating near field DOA and Range Estimates

Two Frequency-Domain methods have been studied:

Frequency-Domain Method based on WVD (Wigner-Ville Distribution) and Fresnel approximation2-D MUSIC based algorithm

Time-domain processing based on direct time-delay estimation between sensors from singular vectors is possible


2D MUSIC approachn sources located at (Ri,θi)Array of m sensors, with spacing d, with aperture of (m-1)dRange Information is given in terms of reference sensor #1No Fresnel approximation


2D MUSIC approach


Processing for Near Field Data

Use Time-Reversal Matrix as a covariance matrix KH(ω)K(ω) Signal/Noise subspace is same for both response matrix and Time-Reversal MatrixProcessing is done for different freqs

Frequencies used: [932 — 1050 Hz]then mean is takenReceiver spacing: d = λ/2, where λ corresponds to highest frequency


Single Target6 Sources, 9 cm apart

15 Receivers, 2 cm apart

Peak Values are picked


Two Targets6 Sources,9 cm apart



Two targets of same size and symmetric w.r.t. array


Two Targets15 Sources,6 cm apart




Effect of Number of Receivers

15 Sources,6 cm apart23 Receivers, 4 cm apart

15 Sources,6 cm apart46 Receivers, 2 cm apart


AccomplishmentsDeveloped three sensor experiment to study multimodal processing

Developed new metal detector and a radarInvestigated two burial scenariosShowed responses for all the sensors over a variety of targetsDemonstrated possible feature for multimodal/cooperative processing

Developed reverse-time experiments, models, and processingDemonstrated focusingDemonstrated enhancement of mine signatureDemonstrated reverse-time imaging on numerical and experimental data

Buried structuresDeveloped numerical model for a buried structureDemonstrated two possible configurations for a sensor


PlansThree sensor experiment

Incorporate reverse-time focusing and imagingMore burial scenarios based on inputs from the signal processing.

More/Stronger clutterDistribution of targets and clutterClose proximity between clutter and targets

Look for more connections between the sensor responses that can be exploited for multimodal/cooperative imaging/inversion/detection algorithmsDevelop multimodal/cooperative imaging/inversion algorithms

Reverse-time processingImprove experiments (Characterize/improve seismic sources)Perform experiments to improve demonstration of reverse time imagingImprove reverse-time imaging algorithmsInvestigate the use of reverse-time ideas to characterize the inhomogeneity of the ground

Buried StructuresOther scenariosSignal Processing

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