1. Multi-modal Adaptive Land Mine Detection Using Ground-Penetrating Radar (GPR) and Electro-Magnetic Induction (EMI) METAL PLASTIC DARPA-ARO MURI Jay A. Marble and Andrew E. Yagle

2. Outline 1.Application Overview 1.1 Data Collection 1.2 Metal and Plastic Landmines 2. Sensor Phenomenology 2.1 Ground Penetrating Radar (GPR) 2.2 Electromagnetic Induction (EMI) 2.3 Overview of Approach 3. Metal Landmine Detection 3.1 GPR Signature Features 3.2 EMI Signature Features 4. Plastic Landmine Detection 4.1 Plastic Landmine Detection Difficulty 4.2 Hyperbola Flattening Transform 4.3 GPR Signature of Plastic Landmines 4.4 Metal Firing Pin Detection 5. Adapting to Changes in Environment 6. Current Progress

3. Bandwidth: 500MHz - 2GHz Depth Resolution: Free Space - 10cm (4”) Soil (  r =3) - 5.7cm (2.3”) 1. Application Overview 1.1 Data Collection GPR Facts EMI Facts 8 11 1 3 5 7 9 13 15 17 19 2 4 6 10 12 14 16 18 1 2 8 7 3 4 5 6 9 10 16 15 11 12 13 14 20 Sampling: Along Track: 5cm Cross Track: 17.5cm Swath: 2.8m Sampling: Along Track: 5cm (2”) Cross Track: 15cm (6”) Swath: 3.0m Operating: 75 Hz Frequency EMI Coils GPR Antennae USArmy Mine Hunter / Killer System Database: 11000m 2

4. 1. Application Overview 1.1 Data Collection

5. Type: M-15 Metal Casing Burial Depth: 3” Width: 13” Height: 5.9” M-21 Metal Casing Burial Depth: 1” Width: 13” Height: 8.1” Type: TM-62M Metal Casing Burial Depth: 2” Width: 13” Height: 5.9” Metal Landmines 1. Application Overview 1.2 Metal Mines Database Contains: 70 metal cased mines buried from 0” to 3” (Shallow). 93 metal cased mines buried from 3” to 6” (Deep).

6. Type: VS1.6 Plastic Casing Burial Depth: 6” Width: 8.6” Height: 3.5” Type: TMA-4 Plastic Casing Burial Depth: 2” Width: 11” Height: 4.3” Type: TM-62P Plastic Casing Burial Depth: 2” Width: 13” Height: 5.9” Type: VS2.2 Plastic Casing Burial Depth: 1” Width: 9” (.23m) Height: 4.5” (.115m) Type: M-19 Plastic Width: 0.33m Height: 3.5” Plastic Landmines 1. Application Overview 1.2 Plastic Mines Database Contains: 156 Shallow 265 Deep

7. NOT LANDMINES LANDMINES How to discriminate between landmines and other objects using GPR and EMI ? GOAL: To determine presence vs. absence of land mines vs. other metal objects USING: Both GPR and EMI data (multi-modal detection algorithm) 1. Application Overview

8. Outline 1.Application Overview 1.1 Data Collection 1.2 Metal and Plastic Landmines 2. Sensor Phenomenology 2.1 Ground Penetrating Radar (GPR) 2.2 Electromagnetic Induction (EMI) 2.3 Overview of Approach 3. Metal Landmine Detection 3.1 GPR Signature Features 3.2 EMI Signature Features 4. Plastic Landmine Detection 4.1 Plastic Landmine Detection Difficulty 4.2 Hyperbola Flattening Transform 4.3 GPR Signature of Plastic Landmines 4.4 Metal Firing Pin Detection 5. Adapting to Changes in Environment 6. Current Progress

9. Tx Rx Antenna Module Target Layer 2 Air  f1f1 f2f2 fNfN f3f3 Sampled Frequencies Depth Profile Fourier Transform Target Transmit Pulse Ground Interface Pulse Launch Sample Time Transmitted Frequencies f1f1 f2f2 fNfN 2.1 GPR Phenomenology Continuous, Stepped Frequency Radar 500MHz – 1.5GHz 128 Frequency Steps h d [m]

10. 2.1 GPR Phenomenology (echo from air-ground interface) (echo from buried target) G T – Gain of transmit antenna G R – Gain of receive antenna E R – Electric field strength at the receiver E 0 – Transmitted Electric field strength. h – Height of antenna above ground d – Depth of target below the surface – Wavelength in Free Space  RCS – Target Radar Cross Section (Propagation Constant Above the ground) *This model is for the antenna directly above the buried object.

11. 2.1 GPR Phenomenology Slightly- Conducting Media Approximation

12. - Along Track [m] Depth [inches] -0.500.51 -15 -12 -9 -6 -3 0 3 Synthetic Aperture Antenna Pattern Data collected in time and space. 2.1 GPR Phenomenology - Along Track [m] Depth [inches] -0.500.51 -15 -12 -9 -6 -3 0 3 Simulated Data (“x-t” domain) - Earth’s Surface x z (0,0.5) x z Point Target (0,6”) -

13. 2.1 GPR Phenomenology Unimaged Signature Metal Casing Height: 6” Width: 13” Depth: 6” TM-62M Landmine X Z TM-62M at 6”

14. 2.2 EMI Phenomenology Air Ground Primary Magnetic Field Buried Sphere Current Source Electronics & Sampler Data Storage Simplified EMI System Concept Air Ground Source Secondary Magnetic Field Source H-field Incident Field at Object Metal Object Reaction

15. Air Ground Source Source H-field (x,y,-d) (x,y,h) 2.2 EMI Phenomenology

16. Metal Object Reaction Secondary Magnetic Field prpr pzpz 2.2 EMI Phenomenology * Model assumes a solid spherical target.

17. Induced Magnetic Sources pxpx pzpz * Model no longer assumes a solid spherical target. H 0x – Horizontal magnetic field at the center of the target produced by the source magnetic dipole. H xz – Vertical magnetic field at the receive coil produced by the horizontal induced magnetic dipole. H 0z – Vertical magnetic field at the center of the target produced by the source magnetic dipole. H zz – Vertical magnetic field at the receive coil produced by the vertical induced magnetic dipole. Target Magnetic Polarizability Vector 2.2 EMI Phenomenology

18. EMI Spatial Signature 2.2 EMI Phenomenology

19. Coil Number (Across Track) Along Track 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Depth: 1” Depth: 3” EMI Spatial Signature 2.2 EMI Phenomenology

20. Screener Stage Feature Extraction Stage Discriminant Stage Feature Vector 2.3 Overview of Approach Screener: Points-of-Interest (POI) are detected and reported. This stage must be fast and must detect all landmines, but can have false-alarms. Discriminant: Combines object features into a test statistic. Features: Aspects of the detected objects are characterized in a vector of feature values. POI

21. 2.3 Overview of Approach: Screener Stage Point-of- Interest List

22. 2.3 Overview of Approach: Feature Extraction Index X Location Y Location 1 291456.6558 4227053.1692 2 291382.6225 4227053.3659 3 291354.7422 4227052.5429. N 291309.1396 4227060.2448 GPR Features Depth Width Height RCS EMI Features Magnetic Dipole Moments Decay Rates Extracted EMI Chip EMI Data 4227052.5429 291354.7422 POI List EMI Data Extracted GPR Cube To Discriminant Function Feature Vector

23. Trained Statistic 2.3 Overview of Approach: Discriminant Function The QPD can be thought of as a mapping. The feature vector (x 1,x 2 ) is mapped into a statistic “s” based on the training of the coefficients (c 1,c 2, c 3,c 4,c 5,c 6 ). The feature values are scalar numbers describing object: X1 - Feature Value 1 (Like: object diameter) X2 – Feature Value 2 (Like: object depth) Output Statistic Quadratic Polynomial Discriminant Function (Shown here for 2 features.)

24. Outline 1.Application Overview 1.1 Data Collection 1.2 Metal and Plastic Landmines 2. Sensor Phenomenology 2.1 Ground Penetrating Radar (GPR) 2.2 Electromagnetic Induction (EMI) 2.3 Overview of Approach 3. Metal Landmine Detection 3.1 GPR Signature Features 3.2 EMI Signature Features 4. Plastic Landmine Detection 4.1 Plastic Landmine Detection Difficulty 4.2 Hyperbola Flattening Transform 4.3 GPR Signature of Plastic Landmines 4.4 Metal Firing Pin Detection 5. Adapting to Changes in Environment 6. Current Progress

25. EMI Simple Threshold  -k Imaging (Size/Depth) EMI Polarization Vector & Decay Rate Detection List GPR Data Discriminant Function EMI Data Y/N Proposed Architecture for Metal Landmine Detection Feature Extractor 3. Metal Mines: Algorithm POI Detector Adaptive Environmental Parameter Estimation

26. Azimuth FFT After Azimuth FFT -60-40-200204060 30 40 50 60 70 80 After 2D Phase Compensation -60-40-200204060 30 40 50 60 70 80 (Kx,Kz) Domain after Stolt Interpolation -60-40-200204060 20 30 40 50 60 70 80 Focused Image -1.5-0.500.511.5 -1.6 -1.4 -1.2 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 2D Phase Comp Stolt Interp 2D FFT After Azimuth FFT -60-40-200204060 30 40 50 60 70 80 After 2D Phase Compensation -60-40-200204060 30 40 50 60 70 80 (Kx,Kz) Domain after Stolt Interpolation -60-40-200204060 20 30 40 50 60 70 80 Mechanics of Wavenumber Migration 3. Wavenumber Migration Imaging Place in  -k Format 2D Phase Comp. Stolt Interp. 2D FFT Hyperbolic Point Target Focused Point Target R(k x,  ) D(k x,k z ) R(k x,  )F(k x,,  )

27. Metal Case Height: 6” Width: 13” Depth: 6” TM-62M Landmine l Depth and Azimuth Resolution  r  r  d variation median inches Air 113.94 Dry Sand 4-651.76 Wet Sand 10-30200.88 Dry Clay 2-532.27 Wet Clay 15-40270.76 3.1 GPR Signature B = 1.5GHz f 0 = 1.25GHz  = 60°

28. Unimaged Signature Depth [Inches] Along Track [Inches] Signature before imaging is dominated by the standard hyperbola. Depth can be determined if data is properly calibrated. Size requires imaging to estimate. “Convexity” of signatures is determined by the speed of propagation in the medium. 3.1 GPR Signature

29. Image Depth [Inches] Along Track [Inches] Imaged signature shows reflections from the top and bottom of the landmine. Length of the object can now be estimated from the length of the top and bottom reflections. Height of the object can be estimated from the distance between the two reflections. Depth has been calibrated during the imaging process. 3.1 GPR Signature

30. Image Depth [Inches] Along Track [Inches] Bottom Reflection Top Reflection 6”6” 13” Estimated Depth and Size Depth: 5.7” Length: 11.3” Height: 6.8” Ground Truth Depth: 6” Length: 13” Height: 6” (Dry Clay) About 3 res. cells across target in depth. 3.1 GPR Signature

31. Objects Reported Bottom Object Top Object Depth [Inches] Along Track [Inches] 2 3 1 4 Four objects are identified by setting a threshold and clustering connected pixels. Objects 1 and 2 are clearly above the ground and can be eliminated. Objects 3 and 4 are the top and bottom reflections. 3.1 GPR Signature

32. 6.8” Objects Reported Depth [Inches] Along Track [Inches] 10.8” 12.5” Length is estimated by averaging the lengths of the two reflections. (Est. Length: 11.3”) Height is the distance between the two reflections. (Est. Height: 6.8”) Depth is the distance from the ground surface (0”) to the top reflection. (Est. Depth: 5.7”) 5.7” 3.1 GPR Signature

33. Repeatability Study Ten Signatures Before Imaging 3.1 GPR Signature

34. Repeatability Study Ten Signatures After Imaging 3.1 GPR Signature

35. Repeatability Study Ten Signatures Binarized

36. Length [inches] Height [inches] Number 1126.86.7 211.36.85.6 311.36.85.6 4186.85.6 5146.86.7 611.35.76.7 710.75.76.7 89.36.86.7 911.35.76.7 1010.76.86.7 Note: Depth Sample Spacing: 1.1” Depth [inches] Ground Truth: Depth: 6” Length: 13” Height: 6” 3.1 GPR Signature Repeatability Study

37. Magnetic Polarizability (signal model) (N Samples) (Least Squares Estimator) To compute the H matrix, we must know the depth of the target. 3.2 EMI Signature

38. GPR (Radar) gives depth information EMI (Dipole models) give H matrix values Combining these: Multi-modal detection Synergy: Each helps the other work better 3.2 EMI Signature

39. Induced Magnetic Sources pxpx pzpz 3.2 EMI Signature

40. Iron Sphere Aluminum Plate No Target Present time Amps Target Present Decay Rate Discriminant

41. 3.2 EMI Signature Aluminum Objects Iron Objects Time [ms] Normalized Response Sum of Decaying Exponentials (Prony): N=2 is usually enough Decay Rate Features :

42. 3. Metal Mines Summary Decay Rate Features: Magnetic Polarizability : EMI Features Depth Length Height GPR Features  -k Imaging Features : Other Features:

43. Outline 1.Application Overview 1.1 Data Collection 1.2 Metal and Plastic Landmines 2. Sensor Phenomenology 2.1 Ground Penetrating Radar (GPR) 2.2 Electromagnetic Induction (EMI) 2.3 Overview of Approach 3. Metal Landmine Detection 3.1 GPR Signature Features 3.2 EMI Signature Features 4. Plastic Landmine Detection 4.1 Plastic Landmine Detection Difficulty 4.2 Hyperbola Flattening Transform 4.3 GPR Signature of Plastic Landmines 4.4 Metal Firing Pin Detection 5. Adapting to Changes in Environment 6. Current Progress

44. HFT Detection Algorithm  -k Imaging (Size/Depth) EMI (Firing Pin) Detection List GPR Data Discriminant Function EMI Data Y/N POI Detector Proposed Architecture for Plastic Landmine Detection Feature Extractor 4. Plastic Mines: Algorithm Adaptive Environmental Parameter Estimation

45. 4.1 Plastic Mine Detection GPR Standard Detection Statistic – Standard Deviation Over Depth Bins The standard detection approach is to create the “plan view” image below by taking a standard deviation over depth. Using this statistic there are many false alarms, but most mines are detected. Deeply buried plastic mines, however, are often missed.

46. 3x10 -3 PDF Estimated from Histogram 3x10 -4 -4 -3 Background Statistics PDF Estimated from Histogram 3x10 -4 -4 4.1 Plastic Mine Detection

47. Probability of Detection Probability of False Alarm ROC Curve Deeply Buried VS1.6 (Depth <3”) About 80% of deep VS1.6 plastic mines are detectable. 4.1 Plastic Mine Detection

48. Plastic Landmine (VS1.6) Surface Top of Mine at 6” Soil Stratum l Deeply buried plastic landmines face a low signal-to-noise ratio (SNR). l Strata in the ground can create large radar returns that lead to false alarms. l The Hyperbola Flattening Transform seeks to exploit all the “energy” of the hyperbolic signature. 4.1 Plastic Mine Detection

49. Simulation Original Hyperbola 45° Rotation Simulation Remapping: 1/y y The Hyperbola Flattening Transform converts a hyperbolic signature into a straight line at 45°. 4.2 Hyperbola Flattening Mathematical Description

50. 180° 90° 0° 120° Radon Transform illustration shows a projection for 120° from a circle. 4.2 Hyperbola Flattening Application to Simulated Data The RADON transform creates “projections” by summing along lines. Projections are oriented for 0° to 180°. Radon Transform of the “flattened” hyperbola has a strong maximum at 45° corresponding to the “energy” contained in the hyperbola.

51. 4.2 Hyperbola Flattening Application to Simulated Data

52. 4.2 Hyperbola Flattening Application to Real Data

53. Transform Location of Hyperbolic Signature 4.2 Hyperbola Flattening

54. 4.2 Hyperbola Flattening

55. VS1.6 Along Track Depth The HFT will now be applied as a detector. A small kernel is moved throughout the scene. At each location, the HFT is applied., At each point the HFT is run for several values of the “a” parameter. The maximum result is placed into a detection image. Original Image 4.2 Hyperbola Flattening Algorithm Application

56. VS1.6 The HFT is applied to all locations in the scene. The detection image shown here is the result. Bright pixels correspond to hyperbolas. Hyperbolic signatures have been contrast enhanced, while non-hyperbolas are suppressed. Along Track Depth Hyperbola Detection Image 4.2 Hyperbola Flattening Algorithm Application

57. VS1.6 Along Track Depth Pixels that break a certain threshold are shown. These pixels reveal the locations of the “most hyperbola-like” signals in the scene. The region corresponding to the VS1.6 has been enhanced by the HFT detector. Algorithm Application Hyperbola-like Regions 4.2 Hyperbola Flattening

58. VS1.6 at 1” 4.3 GPR Signature

59. M19 at 5” 4.3 GPR Signature

60. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Coil Number (Across Track) Along Track Firing Pin Detection Landmines contain a small amount of metal in the firing pin. *The data here has been non- linearly altered. (That is, 3 square roots have been applied.) Plastic Metal EMI Data 4.4 Firing Pin

61. VS2.2 at 1” TM-62P at 2”VS1.6 at 1” Firing Pin Detection All These Landmines are Plastic. Nevertheless, an EMI signal is attainable. The sensor sled was lowered to just 2” above the ground. EMI Spatial Signature 4.4 Firing Pin

62. 4. Plastic Mine Summary Decay Rate Features: Magnetic Polarizability : EMI Features Depth? Length Height GPR Features  -k Imaging Features : Other Features: Firing Pin Detection (binary): (detected) (not-detected)

63. Outline 1.Application Overview 1.1 Data Collection 1.2 Metal and Plastic Landmines 2. Sensor Phenomenology 2.1 Ground Penetrating Radar (GPR) 2.2 Electromagnetic Induction (EMI) 2.3 Overview of Approach 3. Metal Landmine Detection 3.1 GPR Signature Features 3.2 EMI Signature Features 4. Plastic Landmine Detection 4.1 Plastic Landmine Detection Difficulty 4.2 Hyperbola Flattening Transform 4.3 GPR Signature of Plastic Landmines 4.4 Metal Firing Pin Detection 5. Adapting to Changes in Environment 6. Current Progress

64. EiEi EsEs EtEt R 12 = EiEi EsEs  1  0  2 =  r  0 5. Adapting to Environmental Changes Measuring Dielectric Constant of a material is done using the reflection coefficient. Reflection Coefficient  r  r variation median Air 1 1 Dry Sand 4-6 5 Wet Sand 10-30 20 Dry Clay 2-5 3 Wet Clay 15-40 27  r is frequency independent for 500 MHz < f < 2.0GHz

65. Reflection Coefficient Solving for  r is non-linear Therefore, estimates of  r are very sensitive to noise in the observations of R 12. 5. Adapting to Environmental Changes

66. 128 Frequencies After Conversion to  r : Sample Mean – Biased Estimate 5. Adapting to Environmental Changes Example – Dry Soil (  r small) Reflection Coefficient for 128 Frequencies is contaminated with Gaussian Noise. Variance at a single frequency is large, so all 128 must be combined in some way to reduce the estimate variance. n~ N (0,0.01) (SNR = 10dB) n’~ X 1 ? (0,3.6)

67. Simple First Attempt at Adaptive Filter Averages  r of 50 locations along track Performed acceptably for  r = 4 Estimate From 128 Frequencies Adaptive Filter Output 5. Adapting to Environmental Changes

68. Estimation of  r is a challenge. Utilize all available information: 128 Frequencies 20 Antennas Multiple Locations Along Track Characterize Noise after Conversion to  r X[i] =  r + n[i] n~? (How is “n” distributed?) 5. Adapting to Environmental Changes Determine Unbiased Estimator for  r given non-Gaussian nature of noise using 128 frequencies (maximum likelihood) Possibly incorporate a priori information (max. a posteriori) Approach to Adaptive Processing of  r Changes

69. Outline 1.Application Overview 1.1 Data Collection 1.2 Metal and Plastic Landmines 2. Sensor Phenomenology 2.1 Ground Penetrating Radar (GPR) 2.2 Electromagnetic Induction (EMI) 2.3 Overview of Approach 3. Metal Landmine Detection 3.1 GPR Signature Features 3.2 EMI Signature Features 4. Plastic Landmine Detection 4.1 Plastic Landmine Detection Difficulty 4.2 Hyperbola Flattening Transform 4.3 GPR Signature of Plastic Landmines 4.4 Metal Firing Pin Detection 5. Adapting to Changes in Environment 6. Current Progress

70. Wavenumber Migration Processor GPR Point Target Simulator Successful Imaging of Metal Landmines Successful Imaging of Plastic Landmines GPR Feature Set Identify Metal Landmine GPR Feature Set Identify Plastic Landmine GPR Feature Set Automated Extraction of GPR Metal Features Automated Extraction of GPR Plastic Features Plastic Landmine Detection Evaluate Baseline Performance with ROC Curve Implement the Hyperbola Flattening Transform Enhance Processing Speed of the HFT Evaluate HFT Performance using ROC Curves

71. 6. Current Progress Physical Signal Modeling EMI Simple Target Simulator (dipole induction) Study effect of soil conductivity on measured signature. EMI Feature Set Identify Metal Landmine EMI Feature Set P Use Least Squares to Estimate Magnetic Polarization Features P Measure decay rates of iron and aluminum objects. Identify Firing Pin Detection Features Spectral Noise Whitener for Firing Pin Detection Automated Extraction of EMI Metal Features Automated Extraction of EMI Firing Pin Features

72. Adaptive Estimation of  r Estimation of  r from GPR scattering measurements. Determine statistical model of noise in  r observations. Investigate MLE and MAP estimators for  r 6. Current Progress