Multi-modal Adaptive Land Mine Detection Using Ground-Penetrating Radar (GPR) and Electro-Magnetic...

Multi-modal Adaptive Land Mine Detection UsingGround-Penetrating Radar (GPR) and

Electro-Magnetic Induction (EMI)

METAL

PLASTIC

DARPA-AROMURI

Jay A. Marble and Andrew E. Yagle

Outline

1. Application Overview 1.1 Data Collection 1.2 Metal and Plastic Landmines2. Sensor Phenomenology 2.1 Ground Penetrating Radar (GPR) 2.2 Electromagnetic Induction (EMI) 2.3 Overview of Approach3. Metal Landmine Detection 3.1 GPR Signature Features 3.2 EMI Signature Features4. 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 Detection5. Adapting to Changes in Environment6. Current Progress

Bandwidth: 500MHz - 2GHz

Depth Resolution: Free Space - 10cm (4”) Soil (r=3) - 5.7cm (2.3”)

1. Application Overview1.1 Data Collection

GPR Facts EMI Facts

8

111 3 5 7 9 13 15 17 19

2 4 6 10 12 14 16 18

1 2 873 4 5 6 9 10 161511 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 HzFrequency

EMICoils

GPRAntennae

USArmy Mine Hunter / Killer System

Database: 11000m2

1. Application Overview1.1 Data Collection

Type: M-15Metal CasingBurial Depth: 3”Width: 13”Height: 5.9”

M-21Metal CasingBurial Depth: 1”Width: 13”Height: 8.1”

Type: TM-62MMetal CasingBurial Depth: 2”Width: 13”Height: 5.9”

Metal Landmines

1. Application Overview1.2 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: VS1.6Plastic CasingBurial Depth: 6”Width: 8.6”Height: 3.5”

Type: TMA-4Plastic CasingBurial Depth: 2”Width: 11”Height: 4.3”

Type: TM-62PPlastic CasingBurial Depth: 2”Width: 13”Height: 5.9”

Type: VS2.2Plastic CasingBurial Depth: 1”Width: 9” (.23m)Height: 4.5” (.115m)

Type: M-19PlasticWidth: 0.33mHeight: 3.5”

Plastic Landmines

1. Application Overview1.2 Plastic Mines

Database Contains: 156 Shallow 265 Deep

NOT LANDMINES

LANDMINES

How to discriminate between landminesand other objects using GPR and EMI ?

GOAL: To determine presence vs. absence of land mines vs. other metal objectsUSING: Both GPR and EMI data (multi-modal detection algorithm)

1. Application Overview

Outline


-0.5 0 0.5 1 1.5

Tx RxAntenna Module

Target

Layer 2

Air

f1 f2 fNf3

SampledFrequencies

DepthProfile

FourierTransform

Target

TransmitPulse

GroundInterface

PulseLaunch

SampleTime

Transmitted Frequencies

f1

f2

fN

2.1 GPR Phenomenology

Continuous, Stepped Frequency Radar500MHz – 1.5GHz

128 Frequency Steps

h

d

[m]

khjdjd

RCSRT eeeTTdh

GGE 222

2112

2

220 44


khjRT

R eRh

GGEE 2

12

2

220 44

(echo from air-ground interface)

(echo from buried target)

GT – Gain of transmit antennaGR – Gain of receive antennaER – Electric field strength at the receiver E0 – Transmitted Electric field strength.h – Height of antenna above groundd – Depth of target below the surface – Wavelength in Free SpaceRCS – Target Radar Cross Section

002 k (Propagation Constant

Above the ground)

*This model is for the antenna directly above the buried object.


2

1

22

2

112

1

2

1

22

2

112

1

r

rR

1

1

11

12

r

T

1

221

r

T

1

1

212

r Slightly-

Conducting Media

Approximation

-

Along Track [m]

Dep

th [

inch

es]

-0.5 0 0.5 1

-15

-12

-9

-6

-3

0

3

Synthetic Aperture

AntennaPattern

Data collected in time and space.


-

Along Track [m]

Dep

th [

inch

es]

-0.5 0 0.5 1

-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”)

-


Unimaged Signature

Metal CasingHeight: 6”Width: 13”Depth: 6”

TM-62M Landmine

X

Z

TM-62M at 6”

2.2 EMI Phenomenology

Air

Ground

Primary MagneticField

BuriedSphere

CurrentSource

Electronics& Sampler

DataStorage

I

V+

_

EMI Wire Coil

I

V+

_

EMI Wire Coil Simplified EMISystem Concept

Air

Ground

Source

SecondaryMagnetic

Field

Source H-field Incident Field at Object Metal Object Reaction

duruJeum

zyxH hd

u

z )(2

),,( 0)(

0 21

30

021

duruJeum

zyxH hd

u

r )(2

),,( 1)(

0 21

210

021

)( 111122

1 ju

)( 222222

2 ju

Air

Ground

Source

Source H-field

(x,y,-d)

(x,y,h)


),,(),,,(2 03

ssszssz zyxHaPam

),,(),,,(2 03

sssrssr zyxHaPam

))sinh()cosh()(sinh())cosh()(sinh(

))sinh()cosh()(sinh())cosh()(sinh(2),,,(

20

20

s

sss aP

)( ssia

Metal Object Reaction

SecondaryMagnetic

Field

pr

pz

duruJeum

zyxH hz

u

zzz )(

2),,( 0

)(

0 21

321

duruJeum

zyxH hz

u

rrz )(

2),,( 1

)(

0 21

21 21


* Model assumes a solid spherical target.

zzzzxxzx pHHpHHv 00

InducedMagnetic

Sources

px

pz

* Model no longer assumes a solid spherical target.

H0x – Horizontal magnetic field at the center of the target produced by the source magnetic dipole.

Hxz – Vertical magnetic field at the receive coil produced by the horizontal induced magnetic dipole.

H0z – Vertical magnetic field at the center of the target produced by the source magnetic dipole.

Hzz – Vertical magnetic field at the receive coil produced by the vertical induced magnetic dipole.

z

x

p

p TargetMagneticPolarizabilityVector


EMI Spatial

Signature


Coi

l Num

ber

(Acr

oss

Tra

ck)

Along Track

12345678910111213141516

Depth: 1”

Depth: 3”

EMI Spatial

Signature


Screener Stage

FeatureExtraction 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-InterestList

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

4227052.5429291354.7422

POI List EMI Data

Extracted GPR Cube

To Discriminant

Function

Feature Vector

Tra

ined

Sta

tistic

2.3 Overview of Approach:Discriminant Function

• The QPD can be thought of as a mapping. The feature vector (x1,x2) is mapped into a statistic “s” based on the training of the coefficients (c1,c2,c3,c4,c5,c6).

• The feature values are scalar numbers describing object: X1 - Feature Value 1 (Like: object diameter) X2 – Feature Value 2 (Like: object depth)

OutputStatistic

Quadratic Polynomial Discriminant Function(Shown here for 2 features.)

Outline


EMISimple

Threshold -kImaging

(Size/Depth)

EMIPolarization

Vector& Decay

Rate DetectionList

GPR DataDiscriminant

Function

EMI Data

Y/N

Proposed Architecture for Metal Landmine Detection

Feature Extractor

3. Metal Mines: Algorithm

POI Detector

Adaptive EnvironmentalParameter Estimation

Azimuth

FFT

After Azimuth FFT

-60 -40 -20 0 20 40 60

30

40

50

60

70

80

After 2D Phase Compensation

-60 -40 -20 0 20 40 60

30

40

50

60

70

80

(Kx,Kz) Domain after Stolt Interpolation

-60 -40 -20 0 20 40 60

20

30

40

50

60

70

80

Focused Image

-1.5 -1 -0.5 0 0.5 1 1.5

-1.6

-1.4

-1.2

-1

-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 -20 0 20 40 60

30

40

50

60

70

80

After 2D Phase Compensation

-60 -40 -20 0 20 40 60

30

40

50

60

70

80

(Kx,Kz) Domain after Stolt Interpolation

-60 -40 -20 0 20 40 60

20

30

40

50

60

70

80

Mechanics ofWavenumber

Migration

3. WavenumberMigration Imaging

Place in -k

Format

2D Phase Comp.

StoltInterp.

2DFFT

HyperbolicPointTarget

FocusedPoint

Target

R(kx,) D(kx,kz)R(kx,)F(kx,,)

Metal CaseHeight: 6”Width: 13”Depth: 6”

TM-62M Landmine Depth and Azimuth Resolution

r r d

variation median inches

Air 1 1 3.94

Dry Sand 4-6 5 1.76

Wet Sand 10-30 20 0.88

Dry Clay 2-5 3 2.27Wet Clay 15-40 27 0.76

B

c rd 2

/

02

/

f

c ra

3.1 GPR Signature

B = 1.5GHzf0 = 1.25GHz = 60°

Unimaged Signature

De

pth

[In

che

s]

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

De

pth

[In

che

s]


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

De

pth

[In

che

s]


BottomReflection

TopReflection

6”

13”

Estimated Depth and Size

Depth: 5.7” Length: 11.3” Height: 6.8”

Ground Truth

Depth: 6” Length: 13” Height: 6”

3r (Dry Clay)

About 3 res. cells across target in depth.

3.1 GPR Signature

Objects Reported

BottomObject

TopObject

De

pth

[In

che

s]


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

6.8”

Objects Reported

De

pth

[In

che

s]


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 SignaturesBefore Imaging

3.1 GPR Signature

Repeatability Study

Ten SignaturesAfter Imaging

3.1 GPR Signature

3.1 GPR Signature

Repeatability Study

Ten SignaturesBinarized

Length [inches]

Height[inches]Number

1 12 6.8 6.7

2 11.3 6.8 5.6

3 11.3 6.8 5.6

4 18 6.8 5.6

5 14 6.8 6.7

6 11.3 5.7 6.7

7 10.7 5.7 6.7

8 9.3 6.8 6.7

9 11.3 5.7 6.7

10 10.7 6.8 6.7

Note: Depth Sample Spacing: 1.1”

Depth[inches]

Ground Truth: Depth: 6” Length: 13” Height: 6”

3.1 GPR Signature

Repeatability Study

Magnetic Polarizability

npHv

vHHHp

pTT

y

x 1

ˆ

ˆ

np

pHHHHv

z

xzzzxzx

00

(signal model)

(N Samples)

(Least Squares Estimator)

zzzzxxzx pHHpHHv 00

•To compute the H matrix, we must know the depth of the target.

3.2 EMI Signature

z

x

p

p

• 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

InducedMagnetic

Sources

px

pz

0.669ˆ xp

5.324ˆ zp

3.2 EMI Signature

3.2 EMI Signature

Iron Sphere

Aluminum Plate

No Target Present

time

AmpsTarget Present

Decay Rate Discriminant

3.2 EMI Signature

Aluminum Objects

Iron Objects

Time [ms]

No

rma

lize

d R

esp

on

se

tN

nn

neAtr

1

)(

• Sum of Decaying Exponentials (Prony):

• N=2 is usually enough

• Decay Rate Features:

1A

2A12

3. Metal Mines Summary


1A

2A12

xp zp• Magnetic Polarizability:

EMI Features

Depth Length

Height

GPR Features

• -k Imaging Features:

• Other Features:

RCS

Outline


HFTDetectionAlgorithm

-kImaging

(Size/Depth)

EMI(Firing Pin)

DetectionList

GPR Data

DiscriminantFunction

EMI Data

Y/N

POI Detector

Proposed Architecture for Plastic Landmine Detection

Feature Extractor

4. Plastic Mines: Algorithm

Adaptive EnvironmentalParameter 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

34 Deeply Buried VS1.6 Mines

VS1.6 Max Pixel Histogram

3x10-3

34 Deeply Buried VS1.6 Mines

VS1.6 Max Pixel Histogram3x10-3

PDF Estimated from Histogram

3x10-43x10-4

3x10-3

Background StatisticsPDF Estimated from Histogram

3x10-43x10-4


Pro

babi

lity

of

Det

ecti

on

Probability of False Alarm

ROC Curve

Deeply Buried VS1.6(Depth <3”)

• About 80% of deep VS1.6 plastic mines are detectable.


Plastic Landmine (VS1.6)Surface

Top ofMine at 6”

SoilStratum

Deeply buried plastic landmines face a low signal-to-noise ratio (SNR).

Strata in the ground can create large radar returns that lead to false alarms.

The Hyperbola Flattening Transform seeks to exploit all the “energy” of the hyperbolic signature.


Simulation Simulation

Original Hyperbola 45° Rotation

Simulation Simulation

Remapping: 1/yy

12

2

2

2

a

x

d

y 1xy 1y

x

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.


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.


Application to Simulated Data

4.2 HyperbolaFlattening

Application to Real Data

Transform Location ofHyperbolic Signature


VS1.6

Along Track

Dep

th

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


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

Dep

th

Hyperbola Detection Image


Algorithm Application

VS1.6

Along Track

Dep

th

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 ApplicationHyperbola-like Regions


VS1.6 at 1”

4.3 GPR Signature

M19 at 5”

4.3 GPR Signature

12345678910111213141516

Coi

l Num

ber

(Acr

oss

Tra

ck)

Along Track

Firing PinDetection

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 Metal

EMI Data

4.4 Firing Pin

VS2.2 at 1” TM-62P at 2” VS1.6 at 1”

Firing PinDetection

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 EMI Spatial Signature EMI Spatial Signature

4.4 Firing Pin

4. Plastic MineSummary


1A

2A12

xp zp• Magnetic Polarizability:

EMI Features

Depth? Length

Height

GPR Features

• -k Imaging Features:

• Other Features:

RCS

0

1fp

• Firing Pin Detection (binary):

(detected)

(not-detected)

Outline


EiEs

Et

R12 = Ei

Es

10

2 = r 0

r

rR

1

1

11

12

5. Adapting to Environmental Changes

• Measuring Dielectric Constant of a material is done using the reflection coefficient.

• Reflection Coefficient

r r

variation medianAir 1 1

Dry Sand 4-6 5

Wet Sand 10-30 20Dry Clay 2-5 3

Wet Clay 15-40 27

• r is frequency independent for 500 MHz < f < 2.0GHz

2

12

12

11

11

R

Rr

Reflection Coefficient

• Solving for r is non-linear

• Therefore, estimates of r are very sensitive to noise in the observations of R12.


4rnR 33.012

128 Frequencies

r After Conversion to r:

3.4ˆ r

'4ˆ nr

Sample Mean – Biased Estimate


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.

222.0128

)ˆvar()ˆvar( r

r

n~N(0,0.01) (SNR = 10dB)

n’~X1?(0,3.6)

40r4r

• Simple First Attempt at Adaptive Filter

• Averages r of 50 locations along track

• Performed acceptably for r = 4

r rEstimateFrom 128Frequencies

AdaptiveFilter Output


• 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?)


• 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


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 SetP Use Least Squares to Estimate Magnetic Polarization FeaturesP 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

Multi-modal Adaptive Land Mine Detection Using Ground-Penetrating Radar (GPR) and Electro-Magnetic...

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