detection of obscured targets: signal processing james mcclellan and waymond r. scott, jr. school of...

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 13-Jan-05 Scott/McClellan, Georgia Tech 2

Outline

Introduction 3-D Quadtree Imaging

GPR processing examples

Location with Acoustic/Seismic Arrays Small Number of Receivers (moving) Cumulative Array strategy for imaging

Accomplishments/Plans


Three Sensor ExperimentSensor Adjustments and Features

Adjustable Parameters for all three sensors Frequency range Frequency Resolution Spatial Resolution Integration time/bandwidth Height above ground

Location

Possible Features for sensors EMI

Relaxation frequency Relaxation strength Relaxation shape Spatial response

GPR Primary Reflections Multiple Reflections Depth Spatial Response

Seismic Resonance Reflections Dispersion Spatial 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

Ad-HocArray


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)


GPR Processing

Data taken in frequency domain with network analyzer 500 MHz to 8 GHz

Imaging Quadtree

Multi-resolution Approximate Backprojection

2-D, extend to 3-D Fast like -k algorithms

y

x

2 =

4.5

"A

w = 3mm

2 =

62.

4mil

a

L = 6.75"Antenna Shape


Detection and Region Elimination

Purpose : Distinguish regions consisting coherent scatterers, such as mines, from other regions consisting of only clutter.

Detector exploits the fact that from stage to stage the energy for a coherent target increases relative to total energy while energy of clutter decreases.

)(

)(1

1)|(i

i

S

Sii E

ESSP

Define the ratio of the energy in a sub-volume Si+1 to that in its parent’s Si :

The probability of a target being present in stage i+1 is then given by Bayes Rule:

)()....|().|()( 1111 SPSSPSSPSP iiiii

Initial Simulated Data

Y(c

m)

0 10 20 300

10

20

30

Stage 1 Image

0 10 20 300

10

20

30

Stage 2 Image

0 10 20 300

10

20

30

Stage 3 Image

X(cm)

Y(c

m)

0 10 20 300

10

20

30

Stage 4 Image

X(cm)0 10 20 30

0

10

20

30

X(cm)

Selected Areas

0 10 20 300

10

20

30

0

10

20

30

40

05

1015

2025

30350

5

10

15

20

25

X

Likelihood over the surface--two targets at obj1 = [21;4;-20]; and obj2 = [5;27;-20];

Y

Like

lihoo

d

Then apply a threshold test:


Quadtree Results

Experimental Data from the model mine field

Target : A VS-50 AP Mine buried 1.3 cm deep

010

2030

40

010

2030

400

5

10

15

20

25

X

Likelihood over the surface--REAL DATA obj = [26,11,-1.3];

YLi

kelih

ood

Experimental Raw Data

Y(c

m)

0 10 20 300

10

20

30

Stage 1 Image

0 10 20 300

10

20

30

Stage 2 Image

0 10 20 300

10

20

30

Stage 3 Image

X(cm)

Y(c

m)

0 10 20 300

10

20

30

Stage 4 Image

X(cm)0 10 20 30

0

10

20

30

X(cm)

Selected Areas

0 10 20 300

10

20

30


3D Detection Volume

0

5

10

15

0

5

10

15-16

-14

-12

-10

-8

-6

-4

-2

0

X(cm)

3D view of the detection volume

Y(cm)

Dep

th(c

m)

051015

-16

-14

-12

-10

-8

-6

-4

-2

0

X(cm)

Side view of detection volume

Y(cm)

Dep

th(c

m)

0 5 10 15051015

-16

-14

-12

-10

-8

-6

-4

-2

03D view of the detection volume

X(cm)

Dep

th(c

m)

Y(cm)

Synthetic Data: 1 target at position X = 13, Y = 10, Z = -10

Four quadtree stages, 1 cm step size


Outline

Introduction 3-D Quadtree Imaging

GPR processing examples

Location with Acoustic/Seismic Arrays Small Number of Receivers (moving) Cumulative Array strategy for imaging

Accomplishments/Plans


Seismic Sensor

R adar:R .F. Source,D em odulator, andS ignal P rocesssing

S ignal G enerator

E lastic W aveTransducer

E lasticSurfaceW ave

M ine

E .M . W aves

A irSoil

S N S

Wav

egui

deD isp lacem ents


Home in on a Target

Problem Statement: Use a small number of source & receiver positions to locate

targets, i.e., landmines Minimize the number of measurements

Three phases1. Probe phase: use a tiny 2-D array (rectangle or cross)

Find general target area from reflected waves

2. Adaptive placement of additional sensors Maneuver receiver(s) to increase resolution RELAX/CLEAN recalculates for cumulative sensor array

3. On-top of the target Verify the resonance


Adaptive Sensor Placement

Probe ArrayProbe Array findsgeneral target area

1

2

3Additional sensors are added to the “cumulative array”

Target

Source

On-top forresonance


Wideband RELAX algorithm


Surface Plot for Displays


Examples using Sandbox Data

Single source is used Only the Reverse wave is used in processing

Or, passive listening for Buried structures Remove the Forward wave

Prony analysis, or spatial filter Frequency range

obtained from Spectrum Analysis of measured data Future work:

use RELAX to separate Forward and Reverse waves Cylindrical wave model


Spectrum Analysis via Prony

TS-50 at 1cm VS1.6 at 5cm


Processing Examples

Probe Phase Pick five sensors in a cross pattern Apply CLEAN/RELAX algorithm and plot the

“RELAX” surface over a search grid Maneuver Phase

Add one more sensor at a time Increase Aperture, or Triangulate Spatial resolution increases which narrows

down the search area sfor the target


TS-50 (1cm)

Source at (-20,50)


Move Direction after PROBE: Three likely directions to move when adding one more sensor

Next Measurement


1

2

3

Next Measurement


Further Probing of Direction-1


Further Probing of Direction-3


Another Probing of Direction-1


Sensor is placed at the Peak of Previous Step

On-Top


Yet Another Strategy


Accomplishments Developed three sensor experiment to study multimodal processing

Developed new metal detector and a radar Investigated three burial scenarios Showed responses for all the sensors over a variety of targets Demonstrated possible feature for multimodal/cooperative processing Developed new 3D quadtree strategy for GPR data

Developed seismic experiments, models, and processing Improved experimental measurement by incorporating a Wiener filter Demonstrated reverse-time focusing and corresponding enhancement of mine signature Demonstrated imaging on numerical and experimental data from a clean and a cluttered environment Modified time-reverse imaging algorithms to include near field DOA and range estimates. The algorithms are

verified for both numerical and experimental data with and without clutter. Modified wideband RELAX and CLEAN algorithms for the case of passive buried targets. The algorithms are

verified for both numerical and experimental data with and without clutter. Used RELAX imaging to locate targets with cumulative maneuvering receivers. Developed a vector signal modeling algorithm based on IQML (Iterative Quadratic maximum Likelihood) to

estimate the two-dimensional -k spectrum for multi-channel seismic data. Developed multi-static radar

Demonstrated radar operation with and without clutter objects for four scenarios Buried structures

Developed numerical model for a buried structure Demonstrated two possible configurations for a sensor Made measurement using multi-static radar


Plans

Three sensor experiment (Landmine) Incorporate reverse-time focusing and imaging Incorporate multi-static radar More burial scenarios based on inputs from the signal processors

More/Stronger clutter Distribution of targets and clutter Close proximity between clutter and targets

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

Imaging/inversion/detection algorithms Extend 3-D quadtree algorithm to multi-static GPR data. Investigate the use of reverse-time ideas to characterize the inhomogeneity of the ground Investigate the time reverse imaging algorithm for multi-static GPR data. Investigate the CLEAN and RELAX algorithms for target imaging from reflected data in the

presence of forward waves with limited number of receivers. Investigate joint imaging algorithms for GPR and seismic data.

Buried Structures Experiments with multi-static radar Develop joint seismic/radar experiment Signal Processing

detection of obscured targets: signal processing james mcclellan and waymond r. scott, jr. school of...

Documents

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georgia techhome

georgia techgpr processingdata

coherent target

d array rectangle

total energy

energy of clutter decreases

crossfind general target