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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 jim.mcclellan@ece.gatech.edu 404-894-8325
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MURI Review 13-Jan-05Scott/McClellan, Georgia Tech2 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
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MURI Review 13-Jan-05Scott/McClellan, Georgia Tech3 Three Sensor Experiment Sensor 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
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MURI Review 13-Jan-05Scott/McClellan, Georgia Tech4 Multi-Resolution Processing GPR EMI Seismic Imaging SigProc Imaging Features Decision Process Exploit Correlation Training Detect Classify ID Quadtree Imaging @ increasing Resolution (Eliminate Areas) Target Localization @ specific sites Ad-Hoc Array
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MURI Review 13-Jan-05Scott/McClellan, Georgia Tech5 Quadtree BackProjection Sub-Aperture Formation (Virtual Sensor) Image Patch Dividing (Sub-Patch ) Space-Time Domain Decomposition Image Patch Dividing and Sub-Aperture Formation (Virtual Sensor) Divide and Conquer Strategy Computational Complexity O(N 2 log 2 N)
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MURI Review 13-Jan-05Scott/McClellan, Georgia Tech6 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 Antenna Shape
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MURI Review 13-Jan-05Scott/McClellan, Georgia Tech7 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. Define the ratio of the energy in a sub- volume S i+1 to that in its parent’s S i : The probability of a target being present in stage i+1 is then given by Bayes Rule: Then apply a threshold test:
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MURI Review 13-Jan-05Scott/McClellan, Georgia Tech8 Quadtree Results Experimental Data from the model mine field Target : A VS-50 AP Mine buried 1.3 cm deep
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MURI Review 13-Jan-05Scott/McClellan, Georgia Tech9 3D Detection Volume Synthetic Data: 1 target at position X = 13, Y = 10, Z = -10 Four quadtree stages, 1 cm step size
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MURI Review 13-Jan-05Scott/McClellan, Georgia Tech10 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
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MURI Review 13-Jan-05Scott/McClellan, Georgia Tech11 Seismic Sensor
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MURI Review 13-Jan-05Scott/McClellan, Georgia Tech12 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 phases 1.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
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MURI Review 13-Jan-05Scott/McClellan, Georgia Tech13 Adaptive Sensor Placement Probe Array Probe Array finds general target area 1 2 3Additional sensors are added to the “cumulative array” Target Source On-top for resonance
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MURI Review 13-Jan-05Scott/McClellan, Georgia Tech14 Wideband RELAX algorithm
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MURI Review 13-Jan-05Scott/McClellan, Georgia Tech15 Surface Plot for Displays
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MURI Review 13-Jan-05Scott/McClellan, Georgia Tech16 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
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MURI Review 13-Jan-05Scott/McClellan, Georgia Tech17 Spectrum Analysis via Prony TS-50 at 1cmVS1.6 at 5cm
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MURI Review 13-Jan-05Scott/McClellan, Georgia Tech18 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
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MURI Review 13-Jan-05Scott/McClellan, Georgia Tech19 TS-50 (1cm) Source at (-20,50)
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MURI Review 13-Jan-05Scott/McClellan, Georgia Tech20 Move Direction after PROBE: Three likely directions to move when adding one more sensor Next Measurement
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MURI Review 13-Jan-05Scott/McClellan, Georgia Tech21 1 2 3 Next Measurement
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MURI Review 13-Jan-05Scott/McClellan, Georgia Tech22 Further Probing of Direction-1
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MURI Review 13-Jan-05Scott/McClellan, Georgia Tech23 Further Probing of Direction-3
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MURI Review 13-Jan-05Scott/McClellan, Georgia Tech24 Another Probing of Direction-1
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MURI Review 13-Jan-05Scott/McClellan, Georgia Tech25 Sensor is placed at the Peak of Previous Step On-Top
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MURI Review 13-Jan-05Scott/McClellan, Georgia Tech26 Yet Another Strategy
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MURI Review 13-Jan-05Scott/McClellan, Georgia Tech27 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
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MURI Review 13-Jan-05Scott/McClellan, Georgia Tech28 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
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