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

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

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 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

1. Application Overview 1.1 Data Collection

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).

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

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

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

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]

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.

2.1 GPR Phenomenology Slightly- Conducting Media Approximation

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

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

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

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

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

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

EMI Spatial Signature 2.2 EMI Phenomenology

Coil Number (Across Track) Along Track Depth: 1” Depth: 3” EMI Spatial Signature 2.2 EMI Phenomenology

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

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

2.3 Overview of Approach: Feature Extraction Index X Location Y Location N GPR Features Depth Width Height RCS EMI Features Magnetic Dipole Moments Decay Rates Extracted EMI Chip EMI Data POI List EMI Data Extracted GPR Cube To Discriminant Function Feature Vector

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.)

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

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

Azimuth FFT After Azimuth FFT After 2D Phase Compensation (Kx,Kz) Domain after Stolt Interpolation Focused Image D Phase Comp Stolt Interp 2D FFT After Azimuth FFT After 2D Phase Compensation (Kx,Kz) Domain after Stolt Interpolation 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,,  )

Metal Case Height: 6” Width: 13” Depth: 6” TM-62M Landmine l Depth and Azimuth Resolution  r  r  d variation median inches Air Dry Sand Wet Sand Dry Clay Wet Clay GPR Signature B = 1.5GHz f 0 = 1.25GHz  = 60°

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

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

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

Objects Reported Bottom Object Top Object Depth [Inches] Along Track [Inches] 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

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

Repeatability Study Ten Signatures Before Imaging 3.1 GPR Signature

Repeatability Study Ten Signatures After Imaging 3.1 GPR Signature

Repeatability Study Ten Signatures Binarized

Length [inches] Height [inches] Number Note: Depth Sample Spacing: 1.1” Depth [inches] Ground Truth: Depth: 6” Length: 13” Height: 6” 3.1 GPR Signature Repeatability Study

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

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

Induced Magnetic Sources pxpx pzpz 3.2 EMI Signature

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

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 :

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

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

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

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.

3x10 -3 PDF Estimated from Histogram 3x Background Statistics PDF Estimated from Histogram 3x Plastic Mine Detection

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

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

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

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.

4.2 Hyperbola Flattening Application to Simulated Data

4.2 Hyperbola Flattening Application to Real Data

Transform Location of Hyperbolic Signature 4.2 Hyperbola Flattening

4.2 Hyperbola Flattening

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

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

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

VS1.6 at 1” 4.3 GPR Signature

M19 at 5” 4.3 GPR Signature

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

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

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)

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

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 Wet Sand Dry Clay Wet Clay  r is frequency independent for 500 MHz < f < 2.0GHz

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

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)

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

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

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

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

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

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