detection of obscured targets: signal processing
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Detection of Obscured Targets: Signal Processing. James McClellan and Waymond R. Scott, Jr. School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta, GA 30332-0250 [email protected] 404-894-8325. Outline. Introduction - PowerPoint PPT PresentationTRANSCRIPT
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
MURI Review 3-Aug-05 Scott/McClellan, Georgia Tech 2
Outline
Introduction Location with Acoustic/Seismic Arrays
Maneuvering Array (3x10) Cumulative Array strategy for imaging
GPR and Quadtree Region Elimination
Accomplishments/Plans
MURI Review 3-Aug-05 Scott/McClellan, Georgia Tech 4
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
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Outline
Introduction Location with Acoustic/Seismic Arrays
Maneuvering Array (3x10) Cumulative Array strategy for imaging
GPR and Quadtree Region Elimination
Accomplishments/Plans
MURI Review 3-Aug-05 Scott/McClellan, Georgia Tech 7
Ground Contacting Seismic Sensor Array Deployed on a Small Robotic Platform
Source Platform
Sensor Platforms
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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 small 2-D array (rectangle or cross)
Find general target area by imaging with reflected waves
2. Adaptive placement of additional sensors Maneuver receiver(s) to increase resolution Use “Theory of Optimal Experiments”
3. On-top of the target End-game: Verify the resonance
MURI Review 3-Aug-05 Scott/McClellan, Georgia Tech 9
Adaptive Sensor Placement
Probe ArrayProbe Array findsgeneral target area
1
2
3Additional sensors are added to the “cumulative array”
Target
Source
On-top forresonance
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TS-50 (1cm)
Source at (-20,50)
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1
2
3
Next Measurement
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Maneuver the Entire Array
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Wave Separation by Prony Reverse Wave
Imaging algorithm Data Model ML solution for target position estimates Performance bounds for position estimates
Next optimal Array Position D-optimal Design Constrained Optimization
Steps in Optimal Maneuver
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Data Model
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Target Position Estimate
The Cramer-Rao lower bound (CRLB) provides a lower bound for the variances of the unbiased estimators
CRLB requires the inverse of the Fisher information matrix
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Fisher Information Matrix for Position Estimate
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The theory of optimal experiments uses various measures of the Fisher information matrix to produce decision rules Determinant Trace Maximum value along the diagonal
D-optimal Design uses the determinant
X. Liao and L. Carin, “Application of the Theory of Optimal Experiments to Adaptive Electromagnetic-Induction Sensing of Buried Targets,'' IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 26 , no.8, pp. 961−972, August 2004.
Theory of Optimal Experiments
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Target position is estimated from reflected seismic energy Source is nearby Need separation between forward and reverse waves
Array position has to be between source and targets always Landmines reflections are not “omni-directional”
Next array position has to be Constrained “Between” source and estimated target position Two ways to implement constrained optimization
Circle constraint, or Penalty Function
Unique Problems for Seismic
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Constrained optimization for next array position
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Single TS-50 landmine is buried at 1cm Reflected waves are separated at each position
by using a line array of 15 sensors (3cm apart) Actual receiver is a single sensor (Synthetic Array) Measurements are grouped into line arrays
From each line array, three sensors at equal distance positions are chosen With three line arrays, an array of nine sensors is
available for 2-D imaging Inverse of the cost function is plotted for imaging
algorithm
Experimental Data Setup
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Seismic Detection of VS-1.6 AT Landmine Buried 5cm Deep in Experimental Model
Landmine (22.2 cm diameter) buried 110 cm from first measurement location in 171 cm linear scan.
5 measurements with center line of sensor array along a line over the burial location.
Compressional, Rayleigh Surface, and Reflected Waves.
Resonance of Landmine-Soil System.
Interactions of incident waves with buried landmine evident in measured data.
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12
34
Wave Separation at each iteration: along top-most line array
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ACTUAL TARGET : (135,135) ± 5ESTIMATE (102,120)
First Iteration
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Circle constraintR=30cm Movement Penalty
Next Array Position
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Values calculated on half circle
Next array position values calculated on half circle of radius=30cm
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ALONE CUMULATIVE
ACTUAL TARGET : (135,135) ± 5ESTIMATE: ALONE (100,127), CUM(112,127)
Second Iteration
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Values calculated on half circle
Next Array Position
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Four Iterations: Cumulative Imaging
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2D array (3 X 10): Three lines having 10 sensors each
Sensors are ground contacting accelerometers LabView is used to control the movement of
array and seismic source firing and interface with MATLAB
Processing algorithms are implemented in MATLAB
Target is a VS1.6 mine buried at 5cm
Implementation
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Experimental Setup of Seismic Landmine Detection Measurements
3 by 10 Element Array of Ground-
Contacting Sensors
Seismic Source
VS-1.6 AT Landmine (5cm deep)
50 Tons of Damp, Compacted Sand
Scan Region of 2m by 2m
45 cm
174 cm
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Target is localized in four iterations “Bracket” start
Each iteration takes 10 secs. for processing and 30 secs. for data acquisition
Total time for four iterations is 2 minutes
Typical raster scan takes a few hours to locate the target with a large number of measurements
EXPERIMENTAL SETUP
Demo in Sandbox
MOVIE of Experiment
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Four Iterations
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Outline
Introduction Location with Acoustic/Seismic Arrays
Maneuvering Array (3x10) Cumulative Array strategy for imaging
GPR and Quadtree Region Elimination
Accomplishments/Plans
MURI Review 3-Aug-05 Scott/McClellan, Georgia Tech 47
Detecting non-target areas by GPRΘ : Beamwidth of the transmitter and receiver antennas
dtr : Transmitter – Receiver Distance
h : Antenna Height
If there is no detection at this location
Shaded region does not contain targets
Instead of one step size.
Move transmitter by trdh tan2
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Results from the GPR Air Measurements
Shotput in Air; Measured Data
10 20 30 40 50 60 70 80 90
100
200
300
400
500
600
700
800
900-200
-180
-160
-140
-120
-100
-80
-1 -0.5 0 0.5 1
x 10-9
-0.05
0
0.05
0.1
0.15
time(seconds)
Double Differentiated Gaussian Pulse
)(12FAPQ • Threshold :
• PFA is a user defined parameter
• σ2 is estimated from the data
Measured Data Theoretical Reference Signal
• Correlate with a delayed and scaled version of the transmitted signal
• Estimate the variance by taking average energy of the signals from non-target areas
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Detection Test
0 200 400 600 800 1000 1200 1400 1600 1800 2000-2
0
2
4
6
8
10
12x 10
-8
Signal Correlation
Threshold
0 200 400 600 800 1000 1200 1400 1600 1800 2000-2
0
2
4
6
8
10
12x 10
-8 Detection Test at X = -72cm
Signal Correlation
Threshold
0 200 400 600 800 1000 1200 1400 1600 1800 2000-1
-0.5
0
0.5
1
1.5x 10
-7 Detection Test at X = -54cm
Signal Correlation
Threshold
No detection, movetrdh tan2
Detection Test for first measurement point
Target Detected !
Apply time delay difference to find the apex of the hyperbola.
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Monostatic Antenna in Homogeneous Medium
tα+1 Δ tα
z0
22
221
22202
21
2201
2202
2220
)12(4
))12()((4
))1((2
))((4
)(2
ctt
zc
tzc
t
zc
tzc
t
Does not depend on target depth!
Find α which indicates how many step size the antenna is away from the target position
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Target Position Found
0 100 200 300 400 500 600 700 800 900 1000-3
-2
-1
0
1
2
3
4x 10
-5 Reading at X = -54
0 100 200 300 400 500 600 700 800 900 1000-3
-2
-1
0
1
2
3
4
5x 10
-5 Reading at X = -52
TDD
tα
tα+1
α = 26 is found and the antenna is moved to target position which is X = 0
MURI Review 3-Aug-05 Scott/McClellan, Georgia Tech 52
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 Developed optimal maneuver algorithm to locate targets with a seismic array 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. 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 Investigated pre-stack migration imaging of multi-static data
Buried structures Developed numerical model for a buried structure Demonstrated two possible configurations for a sensor Made measurement using multi-static radar
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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 Look for more connections between the sensor responses that can be exploited
for multimodal/cooperative imaging/inversion/detection algorithms Imaging/inversion/detection algorithms
Use 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. Develop elimination and end-game strategies for seismic detection with a
maneuvering array Investigate joint imaging algorithms for GPR and seismic data.
Buried Structures & Tunnels Experiments with multi-static radar Develop joint seismic/radar experiment Signal Processing