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Detection of Obscured Targets: Signal Processing

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. Outline. Introduction

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Detection of Obscured Targets: Signal Processing

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  1. 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

  2. Outline • Introduction • Location with Acoustic/Seismic Arrays • Maneuvering Array (3x10) • Cumulative Array strategy for imaging • GPR and Quadtree • Region Elimination • Accomplishments/Plans Scott/McClellan, Georgia Tech

  3. Quadtree Imaging @ increasing Resolution (Eliminate Areas) Target Localization @ specific sites Ad-Hoc Array Multi-Resolution Processing Features Decision Process Exploit Correlation Training GPR Imaging ID Detect Features EMI SigProc Classify Features Seismic Imaging Scott/McClellan, Georgia Tech

  4. Outline • Introduction • Location with Acoustic/Seismic Arrays • Maneuvering Array (3x10) • Cumulative Array strategy for imaging • GPR and Quadtree • Region Elimination • Accomplishments/Plans Scott/McClellan, Georgia Tech

  5. Ground Contacting Seismic Sensor Array Deployed on a Small Robotic Platform Source Platform Sensor Platforms Scott/McClellan, Georgia Tech

  6. 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 • Probe phase: use a small 2-D array (rectangle or cross) • Find general target area by imaging with reflected waves • Adaptive placement of additional sensors • Maneuver receiver(s) to increase resolution • Use “Theory of Optimal Experiments” • On-top of the target • End-game: Verify the resonance Scott/McClellan, Georgia Tech

  7. On-top for resonance Additional sensors are added to the “cumulative array” 3 2 1 Adaptive Sensor Placement Target Source Probe Array Probe Array finds general target area Scott/McClellan, Georgia Tech

  8. TS-50 (1cm) Source at (-20,50) Scott/McClellan, Georgia Tech

  9. Next Measurement 1 2 3 Scott/McClellan, Georgia Tech

  10. Maneuver the Entire Array Scott/McClellan, Georgia Tech

  11. Steps in Optimal Maneuver • 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 Scott/McClellan, Georgia Tech

  12. Data Model Scott/McClellan, Georgia Tech

  13. 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 Scott/McClellan, Georgia Tech

  14. Fisher Information Matrix for Position Estimate Scott/McClellan, Georgia Tech

  15. Theory of Optimal Experiments • 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. Scott/McClellan, Georgia Tech

  16. Unique Problems for Seismic • 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 Scott/McClellan, Georgia Tech

  17. Constrained optimization for next array position Scott/McClellan, Georgia Tech

  18. Experimental Data Setup • 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 Scott/McClellan, Georgia Tech

  19. 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. Scott/McClellan, Georgia Tech

  20. Wave Separation at each iteration: along top-most line array 2 1 3 4 Scott/McClellan, Georgia Tech

  21. First Iteration ACTUAL TARGET : (135,135) ± 5 ESTIMATE (102,120) Scott/McClellan, Georgia Tech

  22. Next Array Position Circle constraint R=30cm Movement Penalty Scott/McClellan, Georgia Tech

  23. Next array position values calculated on half circle of radius=30cm Values calculated on half circle Scott/McClellan, Georgia Tech

  24. Second Iteration ACTUAL TARGET : (135,135) ± 5 ESTIMATE: ALONE (100,127), CUM(112,127) ALONE CUMULATIVE Scott/McClellan, Georgia Tech

  25. Next Array Position Values calculated on half circle Scott/McClellan, Georgia Tech

  26. Four Iterations: Cumulative Imaging Scott/McClellan, Georgia Tech

  27. Implementation • 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 Scott/McClellan, Georgia Tech

  28. 3 by 10 Element Array of Ground-Contacting Sensors Seismic Source 174 cm 45 cm VS-1.6 AT Landmine (5cm deep) 50 Tons of Damp, Compacted Sand Scan Region of 2m by 2m Experimental Setup of Seismic Landmine Detection Measurements Scott/McClellan, Georgia Tech

  29. Demo in Sandbox EXPERIMENTAL SETUP • 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 MOVIE of Experiment Scott/McClellan, Georgia Tech

  30. Four Iterations Scott/McClellan, Georgia Tech

  31. Outline • Introduction • Location with Acoustic/Seismic Arrays • Maneuvering Array (3x10) • Cumulative Array strategy for imaging • GPR and Quadtree • Region Elimination • Accomplishments/Plans Scott/McClellan, Georgia Tech

  32. Move transmitter by Detecting non-target areas by GPR Θ :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. Scott/McClellan, Georgia Tech

  33. Results from the GPR Air Measurements Theoretical Reference Signal Measured Data • Threshold : • PFA is a user defined parameter • σ2 is estimated from the data • 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 Scott/McClellan, Georgia Tech

  34. Detection Test Detection Test for first measurement point No detection, move Target Detected ! Apply time delay difference to find the apex of the hyperbola. Scott/McClellan, Georgia Tech

  35. tα+1Δ tα z0 Monostatic Antenna in Homogeneous Medium Does not depend on target depth! Find α which indicates how many step size the antenna is away from the target position Scott/McClellan, Georgia Tech

  36. Target Position Found tα α = 26 is found and the antenna is moved to target position which is X = 0 TDD tα+1 Scott/McClellan, Georgia Tech

  37. 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 Scott/McClellan, Georgia Tech

  38. 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 Scott/McClellan, Georgia Tech

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