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DARPA-ARO MURI. Multi-modal Adaptive Land Mine Detection Using Ground-Penetrating Radar (GPR) and Electro-Magnetic Induction (EMI) . Jay A. Marble and Andrew E. Yagle. METAL. PLASTIC. Outline. Application Overview 1.1 Data Collection 1.2 Metal and Plastic Landmines

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slide1

DARPA-ARO

MURI

Multi-modal Adaptive Land Mine Detection Using

Ground-Penetrating Radar (GPR) and

Electro-Magnetic Induction (EMI)

Jay A. Marble and Andrew E. Yagle

METAL

PLASTIC

slide2

Outline

  • 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
slide3

1

2

3

4

5

6

7

9

8

10

11

12

13

14

15

16

1

3

5

7

13

9

15

17

11

19

2

4

6

10

12

8

14

16

18

20

1. Application Overview1.1 Data Collection

USArmy Mine Hunter / Killer System

EMI

Coils

GPR

Antennae

EMI Facts

GPR Facts

Bandwidth:

500MHz - 2GHz

Operating: 75 Hz

Frequency

Sampling:

Along Track: 5cm (2”)

Cross Track: 15cm (6”)

Swath: 3.0m

Sampling:

Along Track: 5cm

Cross Track: 17.5cm

Swath: 2.8m

Depth Resolution:

Free Space - 10cm (4”)

Soil (er=3) - 5.7cm (2.3”)

Database:

11000m2

slide5

1. Application Overview1.2 Metal Mines

Metal Landmines

Database Contains: 70 metal cased mines buried from 0” to 3” (Shallow).

93 metal cased mines buried from 3” to 6” (Deep).

Type: TM-62M

Metal Casing

Burial Depth: 2”

Width: 13”

Height: 5.9”

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”

slide6

1. Application Overview1.2 Plastic Mines

Plastic Landmines

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”

Database

Contains:

156 Shallow

265 Deep

Type: VS1.6

Plastic Casing

Burial Depth: 6”

Width: 8.6”

Height: 3.5”

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”

slide7

1. Application Overview

GOAL: To determine presence vs. absence of land mines vs. other metal objects

USING: Both GPR and EMI data (multi-modal detection algorithm)

LANDMINES

NOT LANDMINES

How to discriminate between landmines

and other objects using GPR and EMI ?

slide8

Outline

  • 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
slide9

Transmitted Frequencies

f1

f2

fN

Pulse

Launch

Sample

Time

2.1 GPR Phenomenology

Continuous, Stepped

Frequency Radar

500MHz – 1.5GHz

128 Frequency Steps

Tx

Rx

Antenna

Module

h

Air

Fourier

Transform

Transmit

Pulse

Ground

Interface

Layer 2

d

Target

...

Target

f1

f2

fN

f3

Sampled

Frequencies

Depth

Profile

[m]

slide10

2.1 GPR Phenomenology

(echo from air-ground interface)

(echo from buried target)

  • GT – Gain of transmit antenna
  • GR – Gain of receive antenna
  • ER – Electric field strength at the receiver
  • E0 – Transmitted Electric field strength.
  • h – Height of antenna above ground
  • d – Depth of target below the surface
  • – Wavelength in Free Space

sRCS – Target Radar Cross Section

(Propagation Constant

Above the ground)

*This model is for the antenna directly

above the buried object.

slide11

2.1 GPR Phenomenology

Slightly-

Conducting

Media

Approximation

slide12

3

3

0

0

-3

-3

-6

-6

Depth [inches]

-9

-9

Depth [inches]

-12

-12

-15

-15

Simulated Data

(“x-t” domain)

-0.5

-0.5

0

0

0.5

0.5

1

1

Along Track [m]

Along Track [m]

-

-

-

-

Earth’s

Surface

x

x

Point Target

(0,6”)

(0,0.5)

z

z

2.1 GPR Phenomenology

Data collected in time and space.

Synthetic Aperture

Antenna

Pattern

slide13

2.1 GPR Phenomenology

TM-62M Landmine

Unimaged Signature

TM-62M at 6”

X

Metal Casing

Height: 6”

Width: 13”

Depth: 6”

Z

slide14

Secondary

Magnetic

Field

Source

Air

P

r

i

m

a

r

y

M

a

g

n

e

t

i

c

F

i

e

l

d

A

i

r

G

r

o

u

n

d

Buried

Sphere

Ground

2.2 EMI Phenomenology

Simplified EMI

System Concept

Current

Source

Data

Storage

Electronics

& Sampler

Source H-field

Metal Object Reaction

Incident Field at Object

slide15

Source

Air

Ground

2.2 EMI Phenomenology

(x,y,h)

(x,y,-d)

Source H-field

slide16

Secondary

Magnetic

Field

pz

pr

2.2 EMI Phenomenology

* Model assumes a solid spherical target.

Metal Object Reaction

slide17

Induced

Magnetic

Sources

pz

px

2.2 EMI Phenomenology

* Model no longer assumes a solid spherical target.

Target

Magnetic

Polarizability

Vector

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.

slide18

2.2 EMI Phenomenology

EMI Spatial

Signature

slide19

2.2 EMI Phenomenology

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

EMI Spatial

Signature

Depth: 1”

Depth: 3”

Coil Number

(Across Track)

Along Track

slide20

Feature

Extraction

Stage

Screener

Stage

Discriminant

Stage

Feature

Vector

2.3 Overview of Approach

POI

Screener: Points-of-Interest (POI) are detected and reported. This stage must

be fast and must detect all landmines, but can have false-alarms.

Features: Aspects of the detected objects are characterized in a vector

of feature values.

Discriminant: Combines object features into a test statistic.

slide21

2.3 Overview of Approach:

Screener Stage

Point-of-

Interest

List

slide22

2.3 Overview of Approach:

Feature Extraction

POI List

EMI Data

EMI Data

  • Index X Location Y Location
  • 291456.6558 4227053.1692
  • 2 291382.6225 4227053.3659
  • 3 291354.7422 4227052.5429
  • .
  • .
  • .
  • N 291309.1396 4227060.2448

4227052.5429

291354.7422

Feature Vector

GPR Features

Depth

Width

Height

RCS

EMI Features

Magnetic Dipole Moments

Decay Rates

To

Discriminant

Function

Extracted GPR Cube

Extracted EMI Chip

slide23

Trained Statistic

2.3 Overview of Approach:

Discriminant Function

Quadratic Polynomial Discriminant Function

(Shown here for 2 features.)

  • 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)

Output

Statistic

slide24

Outline

  • 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
slide25

3. Metal Mines:

Algorithm

Adaptive Environmental

Parameter Estimation

EMI Data

EMI

Polarization

Vector

& Decay

Rate

EMI

Simple

Threshold

Detection

List

Y/N

W-k

Imaging

(Size/Depth)

POI Detector

GPR Data

Discriminant

Function

Feature Extractor

Proposed Architecture for Metal Landmine Detection

slide26

Focused Image

0.4

0.2

0

-0.2

-0.4

-0.6

-0.8

-1

-1.2

-1.4

-1.6

-1.5

-1

-0.5

0

0.5

1

1.5

After Azimuth FFT

After Azimuth FFT

After 2D Phase Compensation

After 2D Phase Compensation

(Kx,Kz) Domain after Stolt Interpolation

(Kx,Kz) Domain after Stolt Interpolation

80

80

80

80

80

80

70

70

70

70

70

70

60

60

60

60

60

60

50

50

50

50

50

50

40

40

40

40

40

40

30

30

30

30

30

30

20

20

-60

-60

-40

-40

-20

-20

0

0

20

20

40

40

60

60

-60

-60

-40

-40

-20

-20

0

0

20

20

40

40

60

60

-60

-60

-40

-40

-20

-20

0

0

20

20

40

40

60

60

3. Wavenumber

Migration Imaging

Focused

Point

Target

Mechanics of

Wavenumber

Migration

Hyperbolic

Point

Target

Place in

W-k

Format

2D

Phase

Comp.

2D

FFT

Stolt

Interp.

2D

Azimuth

Stolt

2D

Phase

FFT

Interp

FFT

Comp

R(kx,W)

R(kx,W)F(kx,,W)

D(kx,kz)

slide27

3.1 GPR Signature

TM-62M Landmine

  • Depth and Azimuth Resolution

ere rrd

variation medianinches

Air 1 1 3.94

Dry Sand 4-6 5 1.76

Wet Sand 10-30 20 0.88

Dry Clay 2-5 3 2.27

Wet Clay 15-40 27 0.76

B = 1.5GHz

f0 = 1.25GHz

Q = 60°

Metal Case

Height: 6”

Width: 13”

Depth: 6”

slide28

3.1 GPR Signature

Unimaged Signature

  • 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.

Depth [Inches]

Along Track [Inches]

slide29

3.1 GPR Signature

  • 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.

Image

Depth [Inches]

Along Track [Inches]

slide30

3.1 GPR Signature

Image

  • Estimated Depth and Size
  • Depth: 5.7”
  • Length: 11.3”
  • Height: 6.8”
  • Ground Truth
  • Depth: 6”
  • Length: 13”
  • Height: 6”

13”

6”

Depth [Inches]

Top

Reflection

Bottom

Reflection

(Dry Clay)

Along Track [Inches]

About 3 res. cells across target in depth.

slide31

3.1 GPR Signature

Objects Reported

  • 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.

2

1

3

Top

Object

4

Depth [Inches]

Bottom

Object

Along Track [Inches]

slide32

3.1 GPR Signature

Objects Reported

  • 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”)

10.8”

5.7”

6.8”

Depth [Inches]

12.5”

Along Track [Inches]

slide33

3.1 GPR Signature

Repeatability Study

Ten Signatures

Before Imaging

slide34

3.1 GPR Signature

Repeatability Study

Ten Signatures

After Imaging

slide35

3.1 GPR Signature

Repeatability Study

Ten Signatures

Binarized

slide36

3.1 GPR Signature

Length

[inches]

Height

[inches]

Depth

[inches]

Number

Repeatability

Study

Note:

Depth

Sample

Spacing: 1.1”

Ground Truth:

Depth: 6”

Length: 13”

Height: 6”

slide37

3.2 EMI Signature

Magnetic

Polarizability

(signal model)

(N Samples)

(Least Squares Estimator)

  • To compute the H matrix, we must
  • know the depth of the target.
slide38

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
slide39

Induced

Magnetic

Sources

pz

px

3.2 EMI Signature

slide40

3.2 EMI Signature

Aluminum

Plate

Iron

Sphere

Amps

No Target Present

Target Present

time

Decay Rate Discriminant

slide41

Aluminum Objects

Iron Objects

Normalized Response

Time [ms]

3.2 EMI Signature

  • Sum of Decaying
  • Exponentials (Prony):
  • N=2 is usually enough
  • Decay Rate Features:
slide42

3. Metal Mines Summary

GPR Features

EMI Features

  • Magnetic Polarizability:
  • W-k Imaging Features:

Depth Length

Height

  • Decay Rate Features:
  • Other Features:
slide43

Outline

  • 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
slide44

4. Plastic Mines:

Algorithm

Proposed Architecture for Plastic Landmine Detection

Adaptive Environmental

Parameter Estimation

EMI Data

EMI

(Firing Pin)

HFT

Detection

Algorithm

Detection

List

Y/N

GPR Data

W-k

Imaging

(Size/Depth)

POI Detector

Discriminant

Function

Feature Extractor

slide45

4.1 Plastic Mine Detection

  • 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.

GPR Standard Detection Statistic – Standard Deviation Over Depth Bins

slide46

Background Statistics

PDF Estimated from Histogram

PDF Estimated from Histogram

3x10

3x10

3x10

3x10

-

-

-

-

4

4

4

4

3x10

3x10

-

-

3

3

4.1 Plastic Mine Detection

slide47

4.1 Plastic Mine Detection

ROC Curve

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

Probability of Detection

Deeply Buried VS1.6

(Depth <3”)

Probability of False Alarm

slide48

4.1 Plastic Mine Detection

Surface

Plastic Landmine (VS1.6)

Top of

Mine

at 6”

  • 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.

Soil

Stratum

slide49

y

1/y

4.2 Hyperbola

Flattening

Mathematical Description

Remapping:

Original Hyperbola

45° Rotation

Simulation

Simulation

Simulation

Simulation

  • The Hyperbola Flattening Transform converts a hyperbolic
  • signature into a straight line at 45°.
slide50

4.2 Hyperbola

Flattening

Application to

Simulated Data

  • The RADON transform
  • creates “projections” by
  • summing along lines.
  • Projections are oriented
  • for 0° to 180°.

90°

  • Radon Transform of the
  • “flattened” hyperbola has a
  • strong maximum at 45°
  • corresponding to the “energy”
  • contained in the hyperbola.
  • Radon Transform illustration
  • shows a projection for 120°
  • from a circle.

120°

180°

slide51

4.2 Hyperbola

Flattening

Application to Simulated Data

slide52

4.2 Hyperbola

Flattening

Application to Real Data

slide53

4.2 Hyperbola

Flattening

Transform Location of

Hyperbolic Signature

slide54

4.2 Hyperbola

Flattening

slide55

4.2 Hyperbola

Flattening

Algorithm Application

Original Image

  • 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.

VS1.6

Depth

Along Track

slide56

4.2 Hyperbola

Flattening

Algorithm Application

Hyperbola Detection Image

  • 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.

VS1.6

Depth

Along Track

slide57

4.2 Hyperbola

Flattening

Algorithm Application

Hyperbola-like Regions

  • 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.

VS1.6

Depth

Along Track

slide58

4.3 GPR Signature

VS1.6 at 1”

slide60

4.4 Firing Pin

EMI Data

Firing Pin

Detection

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

Coil Number

(Across Track)

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.)

Along Track

Plastic

Metal

Metal

slide61

4.4 Firing Pin

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

EMI Spatial Signature

EMI Spatial Signature

TM-62P at 2”

VS1.6 at 1”

VS2.2 at 1”

slide62

4. Plastic Mine

Summary

EMI Features

GPR Features

  • Firing Pin Detection (binary):

(detected)

  • W-k Imaging Features:

(not-detected)

Depth? Length

Height

  • Magnetic Polarizability:
  • Other Features:
  • Decay Rate Features:
slide63

Outline

  • 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
slide64

5. Adapting to

Environmental Changes

Ei

  • Reflection Coefficient

R12 =

Es

Ei

Es

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

e1 = e0

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

e2 =er e0

ere 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

Et

slide65

5. Adapting to

Environmental Changes

  • Solving for er is non-linear
  • Therefore, estimates of
  • er are very sensitive to noise
  • in the observations of R12.

Reflection Coefficient

slide66

5. Adapting to

Environmental Changes

Example – Dry Soil (er 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)

After Conversion to er:

n’~X1?(0,3.6)

Sample Mean – Biased Estimate

128 Frequencies

slide67

5. Adapting to

Environmental Changes

Estimate

From 128

Frequencies

Adaptive

Filter Output

  • Simple First Attempt at Adaptive Filter
  • Averages er of 50 locations along track
  • Performed acceptably for er = 4
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5. Adapting to

Environmental Changes

Approach to Adaptive Processing of er Changes

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

  • 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
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6. Current Progress

  • Wavenumber Migration ProcessorGPR
    • 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
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6. Current Progress

  • Physical Signal ModelingEMI
    • 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
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6. Current Progress

Adaptive Estimation of er

Estimation of er from GPR scattering measurements.

Determine statistical model of noise in er observations.

Investigate MLE and MAP estimators for er