Humanitarian Demining with Ultra Wide Band Ground Penetrating Radar

Background Removal in Array-Based UWB Radars for Landmine

Detection

Delft University of Technology, The NetherlandsPublic University of Navarre, Spain

Álvaro Muñoz Mayordomo

Dr. Miguel Ángel Gómez LasoDr. Alexander G. Yarovoy

04/18/23 2

CONTENTSI. INTRODUCTION: The Landmine Trouble Worldwide

II. GROUND PENETRATING RADAR IN HUMANITARIAN DEMINING

III. SCOPE OF THIS THESIS: Clutter Removal

IV. LITERATURE OVERVIEW AND ANALYSIS OF TECHNIQUES

V. METHODS COMPARISON

VI. INFLUENCE OF TECHNIQUES ON LANDMINE DETECTION

VII. CONCLUSIONS AND FUTURE WORK

04/18/23 3


Detection

INTRODUCTION: The Landmine Trouble Worldwide

INTRODUCTION

04/18/23 4


Foremost side effect after wartime Forgotten landmines

• At least 60 million undetected terrestrial landmines spread over countries in every continent

• 70 people injured every day (26000 victims a year)• 90% civilian population• Major problem in agricultural-based regions. • Cause of displacement• Obstacle to reconstruction after hostilities

INTRODUCTION

04/18/23 5


• Humanitarian Demining Restoring land to the population

• Current –manual– humanitarian demining rate ~ 100 thousand/year

• Cost of removing a single landmine 100-300 times higher than production cost.

• Removing 5000 landmines = one dead person and two injured.

• Sanitary expenses = 10 hundred thousand euros per year

INTRODUCTION

04/18/23 6


Traditional Demining Techniques

• Prodders

• Metal Detectors

INTRODUCTION

04/18/23 7


Traditional Demining Techniques

• Mine-Detection dogs

• Ground-engaging machines• Flails• Rollers• Millers and Tillers• Sifters• Dozers and graders

INTRODUCTION

04/18/23 8


Innovative Demining Techniques

• Chemical sensing• Infrared imaging• Biosensing and explosive particle detection • Nuclear and atomic methods• Passive millimeter wave sensors • Acoustic impulses

• Ground Penetrating Radar

INTRODUCTION

04/18/23 9


Detection

GPR IN HUMANITARIAN DEMINING

04/18/23 10


Basic principles

• Time domain or impulse GPR • Discrete pulses of nanosecond duration

• Digitizes GHz sample rates

• Frequency domain GPR • Series of frequency steps • Chirp• Conversion time domain

INTRODUCTION GPR

04/18/23 11INTRODUCTION GPR


Basic principles

•Majority of today's GPR technology based on Impulse Radar •Single echo return at a position n A-scan

•Recording time Depth range•Expressed in Volts

n n nA t s t b t e

0 0.2 0.4 0.6 0.8 1

x 10-8

-1000

-500

0

500

1000

1500

Time [s]

Am

plitu

de [

mV

]

Antenna Crosstalk

Ground Bounce

Target Response

Antenna Crosstalk

n n nA t s t b t e

04/18/23 12INTRODUCTION GPR


Basic principles

• Whole ensemble of A-scans B-scan

• 2D subsurface Propagation time

picture Along-scan Position

• 3D subsurface Propagation time

picture Along-scan Position

Signal Amplitude

04/18/23 13


Basic principles

B-Scan

Position [m]

Tim

e [s

]

0 0.1 0.2 0.3 0.4 0.5 0.6

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x 10-9

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-50

0

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100

B-Scan

Position [m]

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s]

0 0.1 0.2 0.3 0.4 0.5 0.6

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x 10-9

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RAW DATA DATA AFTER SUBTRACTION

INTRODUCTION GPR

04/18/23 14


Basic principles

B-Scan

Position [m]

Tim

e [s

]

0 0.1 0.2 0.3 0.4 0.5 0.6

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DATA AFTER SUBTRACTION

DATA AFTER FOCUSING

INTRODUCTION GPR

04/18/23 15


IRCTR UWB Mini-Array GPR • Global Project named CADMIUM • IRCTR-TNO collaboration • New terrestrial vehicle for landmine detection• Multisensor

• Metal detector • Infrared sensor• GPS system• UWB GPR

INTRODUCTION GPR

04/18/23 16


IRCTR UWB Mini-Array GPR

• Main novelty Modular approach• Independent modules

• Reduction of electronics and number of antennas • Pulse generator 500 kHz• Connected to both TX antenna and RX array

INTRODUCTION GPR

04/18/23 17


IRCTR UWB Mini-Array GPR

• Choice of waveform Major role in GPR detection

• Goals

• Penetration depth: Freqs<1GHz• Resolution several cm: large bandwith 3GHZ• Low early and late ringing

INTRODUCTION GPR

04/18/23 18


Detection

SCOPE OF THIS THESIS: Clutter Removal

INTRODUCTION GPR Clutter Removal

04/18/23 19

SCOPE OF THIS THESIS : Clutter Removal

Ground Bounce and Antenna Effects Mitigation

0 0.2 0.4 0.6 0.8 1

x 10-8

-1000

-500

0

500

1000

1500

Time [s]

Am

plitu

de [

mV

]

0 0.2 0.4 0.6 0.8 1

x 10-8

-10

-5

0

5

10

15After Subtraction

Time [s]

Am

plitu

de [

mV

]

Antenna Crosstalk

Ground Bounce

Target Response

Ground Bounce

Target Response

Antenna Crosstalk

Antenna Crosstalk


04/18/23 20


Dedicated processing Extract the target signal

Deeply buried landmines

Landmine is shallowly buried or laid on the ground

• Target signal and surface signal are close and overlap

Landmine is small or dielectric-made

• Scattering strength is lower


04/18/23 21


• Background Subtraction Topic of this thesis • Radar processing chain BG removal precedes Focusing• Always error while estimating the BG Residues • Level of residues depends on particular BG estimation• Objectives and approach

• Implementation of techniques• Evaluation before Focusing• Selection Online algorithms

Offline algorithms• Evaluation after Focusing


04/18/23 22


LITERATURE SURVEY

ALGORITHM IMPLEMENTATION

ALGORITHM TESTING

ONLINE APPROACH OFFLINE APPROACH

PERFORMANCE STUDY

SCENARIO A SCENARIO B

SCENARIO C SCENARIO D

Signal-Background Ratio Comput. Requirements Signal-Background Ratio Comput. Requirements

EVALUATION AFTER MIGRATION

SCENARIO E

Energy-Background Ratio

SCENARIO C SCENARIO D


04/18/23 23


Detection

LITERATURE OVERVIEW AND ANALYSIS OF TECHNIQUES

INTRODUCTION GPR Clutter Removal ANALYSIS

04/18/23 24INTRODUCTION GPR Clutter Removal ANALYSIS


Description of Test Scenarios

Data set

Scenario features

RoughnessNumber of

TargetsDepth Type of targets

A Flat 1 Surface Metal disk, 10cm

B Flat 7 Surface Metal/Plastic cylinders and pipe 5-10cm

C Quite flat 8 5cm Metal/Plastic/Cylinder, 10cm3 Plastic cylinders, 5.4cm

2 Plastic mines, 13cm

D Very rough 4 5cm Plastic mine 13cm; Plastic mine 8cm; one rock; one screw

E Rough with grass 6 Semi-buried and 5cm

Plastic mines, 12cm

04/18/23 25


1) High Pass Filter2) Exponential Averaging3) Linear Prediction4) Moving Average5) Moving Median6) Weighted Moving Average7) Cylindrical Moving Average8) Shifted and Scaled Background

1) Arbitrary Reference BG2) Frequency Domain3) Time Domain

9) Principal Component Analysis


04/18/23 26

1) FIR High Pass Filter

• Two contiguous A-scans in background calculation

• Equally distributed weights

• High speed • Little memory usage

S. Nagwa, M. Bames, “A moving target detection filter for an ultra-wideband radar”

dx

dy

Scan Direction


10.5n n n n ns t A t b t A t A t

0.5

An(t)

An-1(t)

bn(t)


04/18/23 27

B-Scan

Position [m]

Tim

e [s

]

0 0.1 0.2 0.3 0.4 0.5 0.6

0

1

2

3

4

5

6

7

8

9

10

x 10-9

-15

-10

-5

0

5

10

15




B-Scan

Position [m]

Tim

e [

s]

0 0.1 0.2 0.3 0.4 0.5 0.6

0

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x 10-9

-500

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Scenario A



04/18/23 28



B-Scan

Position [m]

Tim

e [s

]

0 0.2 0.4 0.6 0.8 1 1.2 1.4

0

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9

x 10-9

-4

-3

-2

-1

0

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4


B-Scan

Position [m]

Tim

e [s

]

0 0.2 0.4 0.6 0.8 1 1.2 1.4

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9-200

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-50

0

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Scenario B



04/18/23 29



B-Scan

Position [m]

Tim

e [s

]

0 0.5 1 1.5 2

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10

x 10-9

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-2

0

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6


Position [m]

Tim

e [s

]

B-Scan

0 0.5 1 1.5 2 2.5

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x 10-9

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Scenario C



04/18/23 30



B-Scan

Position [m]

Tim

e [s

]

0 0.2 0.4 0.6 0.8 1

0

1

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3

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5

6

7

8

x 10-9

-2

-1.5

-1

-0.5

0

0.5

1

1.5


B-Scan

Position [m]

Tim

e [s

]

0 0.2 0.4 0.6 0.8 1

0

1

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3

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5

6

7

8

x 10-9

-8

-6

-4

-2

0

2

4

6

8

10

Scenario D



04/18/23 31


2) Exponential Averaging

• BG function of • Previous measurement• Previous BG calculation

• An-1(t), decays in the storage with an exponential

)()1()()( 11 tbtAtb nnn

Zetik, R., Crabbe, S., Krajnak, J., Peyerl, P., Sachs, J., Thoma, R., “Detection and localization of persons behind obstacles using M-sequence through-the-wall radar”


04/18/23 32



• Emphasizes recent events

• Smoothes strong variations

• Low memory and CPU required

• Each estimated BG is stored )()1()()( 11 tbtAtb nnn

Michael Bramberger, Roman Pflugfelder, Bernhard Rinner, Helmut Schwabach, Bernhard Strobl, “Intelligent traffic video sensor: architecture and applications”


04/18/23 33



B-Scan

Position [m]

Tim

e [

s]

0 0.1 0.2 0.3 0.4 0.5 0.6

0

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9

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x 10-9

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0

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B-Scan

Position [m]

Tim

e [s

]

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x 10-9

-50

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α=0.3Scenario A

)()1()()( 11 tbtAtb nnn RAW DATA DATA AFTER

SUBTRACTION


04/18/23 34


3) Linear Prediction

• BG Weighted linear function Previous traces

Future traces

• Generalized Two-sided LP model

• ap- and ap+ Linear prediction coefficients ( ) ( ) ( )n p n p p n pb t a A t a A t ;

Jin-Jen Hsue and Andrew E. Yagle, “Similarities and differences between one-sided and two-sided linear prediction”


04/18/23 35

( ) ( ) ( )n p n p p n pb t a A t a A t ;


• Selection of p Critical in performance

• Estimation valid An-p(t) or No target

response An+p(t)

Thomas C. T. Chan, H. C. So, K. C. Ho, “Generalized two-sided linear prediction approach for land mine detection”


A-scan under process A-scans involved in one background calculation

dx

dy

p

p


04/18/23 36


3) Linear Prediction ( ) ( ) ( )n p n p p n pb t a A t a A t ;

B-Scan

Position [m]

Tim

e [s

]

0 0.2 0.4 0.6 0.8 1 1.2 1.4

0

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9-200

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-50

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p=8cm

B-Scan

Position [m]

Tim

e [s

]

0 0.2 0.4 0.6 0.8 1 1.2

0

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x 10-9

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0

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Scenario B



04/18/23 37


4) Moving Average

• Along-track sliding window N

• An(t) n=N

n=(N-1)/2

• Window size related to length of hyperbolas

1

2

1

2

Nk n

kN

k n

n

A t

b tN

F.P. Haeni, Marc L. Buursink, and John E. Costa, “Ground-penetrating radar methods used in surface-water discharge measurements”


dx

dy

Scan Direction

A-scan under process A-scans involved in one averaging

04/18/23 38


4) Moving Average

• Non-changing part of signal considered as background

• Based on two assumptions• Targets Isolated scatterers • Constant roughness or

smooth changes

1

2

1

2

Nk n

kN

k n

n

A t

b tN

A. G. Yarovoy, P. van Genderen, and L. P. Ligthart, “Ultra-wideband ground penetrating impulse radar”


04/18/23 39


4) Moving Average

Position [m]

Tim

e [s

]

B-Scan

0 0.5 1 1.5 2 2.5

0

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2

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x 10-9

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B-Scan

Position [m]

Tim

e [s

]

0 0.5 1 1.5 2 2.5

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x 10-9

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0

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N=17cm

n kb t mean A t k=1,..,N

Scenario C



04/18/23 40


5) Moving Median • Median replaces Mean• Median of a group of A-scans:

• Aa(tj)< Ab(tj) <…, Ac(tj), j=1,…,NT

• Selection of central value

• bn Compilation of statistic medians for each time sample within the A-scans in window

• Less sensitive to extreme changes than Moving Average

n kb t median A t

k=1,..,N

Adel ElFouly, “Voids investigation at Gabbari Tombs, Alexandria, Egypt using ground penetrating radar technique”


04/18/23 41


5) Moving Median k=1,..,N n kb t median A t

Position [m]

Tim

e [s

]

B-Scan

0 0.5 1 1.5 2 2.5

0

1

2

3

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5

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8

9

10

x 10-9

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0

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600

800

1000

B-Scan

Position [m]

Tim

e [s

]

0 0.5 1 1.5 2 2.5

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10

x 10-9

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0

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80

N=17cmScenario C



04/18/23 42


6) Weighted Moving Average

• Enhances Standard Moving Average Weights

• Weights apply to each time sample • More weight to BG samples• Less weight to signal

samples

• Two averages are needed

1

1

n

i iin

ii

w xX

w

Ö. Yilmaz, Seismic Data Processing, Society of Exploration Geophysicists, Tulsa, 1987


04/18/23 43



Processing SequenceA. Preliminary Moving Average

background subtraction • Small sliding window

B. Hilbert Transform • Envelope Reflectivity

strength• Instantaneous amplitudes

weighting coefficients C. Moving Average using weights

• Large sliding window

1

1

n

i iin

ii

w xX

w

Friedrich Roth, Convolutional Models for Landmine Identification with Ground Penetrating Radar


04/18/23 44



B-Scan

Position [m]

Tim

e [s

]

0 0.2 0.4 0.6 0.8 1 1.2 1.4

0

1

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8

9-200

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0

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300

N=13cm n=3cm

B-Scan

Position [m]

Tim

e [s

]

0 0.2 0.4 0.6 0.8 1 1.2 1.4

0

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x 10-9

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-15

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-5

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5

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15

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25

1

1

n

i iin

ii

w xX

w

Scenario B



04/18/23 45


7a) Shifted and Scaled Arbitrary Reference BG

• Arbitrary bRef(t) signal • Amplitude scale α

• Max and Min bRef(t) Max and Min An(t)

• Time shift tn,ref Ground-air bounces overlap , ,n n ref ref n refb t b t t

;

Friedrich Roth, Convolutional Models for Landmine Identification with Ground Penetrating Radar


04/18/23 46


7a) Shifted and Scaled Arbitrary Reference BG

B-Scan

Position [m]

Tim

e [

s]

0 0.1 0.2 0.3 0.4 0.5 0.6

0

1

2

3

4

5

6

7

8

9

10

x 10-9

-500

-400

-300

-200

-100

0

100

200

300

B-Scan

Position [m]

Tim

e [s

]

0 0.1 0.2 0.3 0.4 0.5 0.6

0

1

2

3

4

5

6

7

8

9

x 10-9

-100

-50

0

50

100

, ,n n ref ref n refb t b t t

Scenario A

RAW DATA AFTER SUBTRACTION


04/18/23 47


7b) Adaptive Shifted and Scaled BG in Freq. Domain

• Original for Stepped Frequency Radar • Impulse Radar Frequency domain FFT • Nonlinear minimization least squares

criterion ,

2

, , ,1

( , ) ( ) ( ) k n i

Kj

i n i n i n k n i n i kk

C x x e

@

,

,

*,

1

arg max ( ) ( ) k n i

n i

Kj

n i n i k n kk

x x e

:

,*

1,

2

1

( ) ( )

( )

n ik

Kj

n i k n kk

n i K

n i kk

x x e

x

:

:

R. Wu, A. Clement, J. Li, E. G. Larsson, M. Bradley, J. Habersat, and G. Maksymonko, “Adaptive ground bounce removal”


04/18/23 48


7b) Adaptive Shifted and Scaled BG in Freq. Domain

B-Scan

Position [m]

Tim

e [s

]

0 0.2 0.4 0.6 0.8 1

0

1

2

3

4

5

6

7

8

x 10-9

-8

-6

-4

-2

0

2

4

6

8

10B-Scan

Position [m]

Tim

e [s

]

0 0.2 0.4 0.6 0.8 1

0

1

2

3

4

5

6

7

8

x 10-9

-3

-2

-1

0

1

2

3

4

,

2

, , ,1

( , ) ( ) ( ) k n i

Kj


C x x e

@

Scenario D



04/18/23 49


7c) Adaptive Shifted and Scaled BG in Time Domain

• Time saving

• Real signals• Single optimization for A-scan (instead of k)

• Nonlinear minimization least squares criterion

2

, , , ,

0

( , ) ( ) ( )T

i n i n i n n i n i n iC S t S t dt @

,

, ,

0

arg max ( ) ( )n i

T

n i n in i nS t S t dt

: ,

0,

2

0

( ) ( )

( )

T

n in i n

n i T

n i

S t S t dt

S t dt

:

:


04/18/23 50


7c) Adaptive Shifted and Scaled BG in Time Domain

B-Scan

Position [m]

Tim

e [s

]

0 0.2 0.4 0.6 0.8 1 1.2 1.4

0

1

2

3

4

5

6

7

8

9-200

-150

-100

-50

0

50

100

150

200

250

300B-Scan

Position [m]

Tim

e [s

]

0 0.2 0.4 0.6 0.8 1 1.2 1.4

0

1

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3

4

5

6

7

8

9

x 10-9

-60

-50

-40

-30

-20

-10

0

10

20

30

40

2

, , , ,

0

( , ) ( ) ( )T


Scenario B



04/18/23 51


8) Cylindrical Moving Average

• 2D Moving Average • Circular averaging area N A-scans

• Along-scan direction• Cross-scan direction

• Spatial window Cylinder (geometry of the problem)

2 2 2

,

1,i j xy

x y R

b t A tN

Jeroen Groenenboom, Alexander Yarovoy, “Data processing and imaging in GPR system dedicated for landmine detection”


04/18/23 52



dx

dy

Scan Direction

A-scan under process A-scans involved in one averaging

Jeroen Groenenboom, Alexander Yarovoy, “Data processing and imaging in GPR system dedicated for landmine detection”

2 2

,1

1,i j xy

x y

b t A tN


04/18/23 53


8) Cylindrical Moving Average 2 2

,1

1,i j xy

x y

b t A tN

B-Scan

Position [m]

Tim

e [s

]

0 0.1 0.2 0.3 0.4 0.5 0.6

0

1

2

3

4

5

6

7

8

9

10

x 10-9

-40

-20

0

20

40

60B-Scan

Position [m]

Tim

e [

s]

0 0.1 0.2 0.3 0.4 0.5 0.6

0

1

2

3

4

5

6

7

8

9

10

x 10-9

-500

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-100

0

100

200

300

Scenario A



04/18/23 54


9) Principal Component Analysis • Any real matrix S Subspaces orthonormal

basis

• U and V unitary matrices • σ1,…,σr ≥ 0 singular values of S (r=rank (S))• vi Vectors in V Principal Components• Sliding window implementation

TS U V 1 rdiag ( , ... , )

1

( )n i ii

b t v

Gilbert Strang, Linear Algebra and its Applications, Harcourt College Publishers, 3rd Edition, 1988


04/18/23 55



N=8.4cm λ=1 component

B-Scan

Position [m]

Tim

e [s

]

0 0.5 1 1.5 2

0

1

2

3

4

5

6

7

8

9

10

x 10-9

-100

-50

0

50

100

150

Position [m]

Tim

e [s

]

B-Scan

0 0.5 1 1.5 2 2.5

0

1

2

3

4

5

6

7

8

9

10

x 10-9

-800

-600

-400

-200

0

200

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600

800

1000

1

( )n i ii

b t v

Scenario C



04/18/23 56


Detection

METHODS COMPARISON

INTRODUCTION GPR Clutter Removal ANALYSIS COMPARISON

04/18/23 57

METHODS COMPARISON

Numerical Criterion Applied

2

,,

2

,,

max

max

S

B

kk L R

k Tk L T R

abs s t

SNBabs b t

0.6 0.8 1 1.2 1.4 1.6 1.8

0

2

4

6

8

10

x 10-9

REGION Rs

REGION Rb

LENGTH L

Sk,τ target signal

bk,τ ground bounce


04/18/23 58

METHODS COMPARISON

-20

-15

-10

-5

0

dB

SBR for a smooth surface (C)

FIREXP. AVL. PRED.MOV. AVMOV-MEDW. MOV. AVSaS Arbit.SaS FREQ.SaS TIMECYLIND. MAVPCA

z


04/18/23 59

METHODS COMPARISON

-12

-10

-8

-6

-4

-2

0

2

dB

SBR for a rough surface (D)

FIREXP. AVL. PRED.MOV. AVMOV-MEDW. MOV. AVSaS Arbit.SaS FREQ.SaS TIMECYLIND. MAVPCA

z


04/18/23 60

METHODS COMPARISON

1) FIR High Pass Filter• Study of performance straightforward No parameters• SBR improvement larger in rough scenario• Time consumption very low

2) Exponential Averaging• Rough surface influence of weighting factor • Time consumption low • Storage previous background calculation

3) Linear Prediction• Large dependence on adjustable parameter p • When different target sizes complicate detection


( ) ( ) ( )n p n p p n pb t a A t a A t ;

)()1()()( 11 tbtAtb nnn


04/18/23 61

METHODS COMPARISON

4) Moving Average• Not able to remove reflections from rough surfaces• Influence of window is not critical in smooth scenarios

5) Moving Median• Generally improves SBR level of Moving Average for same

window length• Large window size compared to hyperbola degradation

6) Weighted Moving Average• Comparison with simple averaging SBR• High SBR can be achieved Accurate selection• Computational burden Double averaging

n kb t mean A t k=1,..,N

n kb t median A t k=1,..,N

1

1

n

i iin

ii

w xX

w


04/18/23 62

METHODS COMPARISON

7a) SaS Arbitrary Reference BG• Slowly changing surface outperforms FIR, MAV and MM• Time consuming

7b) Adaptive SaS BG in Freq. • Outstanding SBR values• Lower time consumption than cylindrical average or WMA

7c) Adaptive SaS BG in Time • Improvement in SBR is high for a rough surface• High time of execution

, ,n n ref ref n refb t b t t

2

, , , ,

0

( , ) ( ) ( )T


,

2

, , ,1

( , ) ( ) ( ) k n i

Kj


C x x e

@


04/18/23 63

METHODS COMPARISON


• Remarkable results for a rough surface• Processing several array lines processing

time

9) Principal Component Analysis • Complicated parametric study• Efficient implementation time reduction

2 2

,1

1,i j xy

x y

b t A tN

1

( )n i ii

b t v


04/18/23 64

METHODS COMPARISON

Algorithm/Technique

Algorithm features

A-scans involved inBackground Model

AlgorithmParameters

Recommended Application

FIR Filtering An(t), An-1(t) None On line

Exponential Averaging An(t), bn-1(t)Weighting factor

αOn line

Two-Sided Linear Prediction

An-p(t), An+p(t)Prediction range

pOn line

Moving Average Ak(t), k=1,…,m Sliding window m On line/Off line

Moving Median Ak(t), k=1,…,m Sliding window m On line/Off line

Moving WeightedAk(t), k=1,…,n

Ak(t), k=1,…,mSliding window nSliding window m

Off line


04/18/23 65INTRODUCTION GPR Clutter Removal ANALYSIS COMPARISON

METHODS COMPARISON

Algorithm/Technique

Algorithm features

A-scans involved inBackground Model

AlgorithmParameters

Recommended Application

Shifted and Scaled Arbitrary

ARef(t) Time delay τAmplitude scale α

Off line

Shifted and Scaled Frequency Domain

ARef(t) Time delay τ

Amplitude scale αSliding window m

On-line/Off-line

Shifted and Scaled Time Domain

ARef(t) Time delay τ

Amplitude scale αSliding window m

Off line

Cylindrical Moving Average

Axy(t), x2+y2<=R2 Averaging radius

R Off line

Principal Components Ak(t), k<=p Sliding window m

Number of components p

On line

04/18/23 66


Detection

INFLUENCE OF TECHNIQUES ON LANDMINE DETECTION

INTRODUCTION GPR Clutter Removal ANALYSIS COMPARISON INFLUENCE

04/18/23 67


• SCENARIO AND DATA SET E

Position [m]

Tim

e [

s]

B-Scan

0 5 10 15 20 25

0

2

4

6

8

x 10-9

-200

0

200


04/18/23 68


• SCENARIO AND DATA SET E

Object Target Location

Position X Position Y Depth

NR22-AP 1 -10 semi

NR22-AP 1 10 semi

NR22-AP 1.5 -10 5cm

NR22-AP 1.5 10 5cm

NR22-AP 1.75 0 semi

NR22-AP 2 0 5cm

Scan line [m]

Arr

ay lin

e [

m]

Moving Median Filter

0.9 1 1.1

-0.2

-0.1

0

0.1

0.2

-10

-5

0


04/18/23 69




04/18/23 70




04/18/23 71


3) Moving Median


04/18/23 72


4) Shifted and Scaled BG in Freq. Domain


04/18/23 73



Scan line [m]Arr

ay li

ne [

m]

0 2 4 6 8 10 12 14 16 18-0.200.2

00.51

Scan line[m]

Arr

ay li

ne[m

]

PCA1

0.5 1 1.5 2 2.5-0.2

0

0.2


04/18/23 74


EBR COMPARISON

Algorithm Energy Feature

Signal Energy

Background Energy

EBR

2-Sided LP 5.5976E+3 448.1885 12.48

FIR Filtering 454.1735 49.0152 9.26

Median Filtering 1.9409E+3 211.9093 9.15

SaS Frequency Domain 3.5014E+3 827.2433 4.23

Principal Components 1.0639E+3 231.1741 4.60


04/18/23 75


Detection

CONCLUSIONS AND FUTURE WORK

INTRODUCTION GPR Clutter Removal ANALYSIS COMPARISON INFLUENCE CONCLUSIONS

04/18/23 76

CONCLUSIONS AND FUTURE WORK• A number of algorithms developed for clutter

removal in GPR

• Difficulty to state quality objectively• Sort of terrain• Roughness• Material/size of targets

• Performance Analysis after BG subtraction SBR

• Performance Analysis after migration EBR

• Computational Analysis Off-line/On-line


04/18/23 77

CONCLUSIONS AND FUTURE WORK• Best performance on smooth surface

SaS in Frequency Domain

• Best performance on rough surface SaS in Time and Frequency Domain Cylindrical Moving Average

• Algorithms suggested for online processing• FIR filtering• Linear Prediction• Exponential Averaging

• Less computationally expensive algorithm FIR filter

• Best performing algorithm after migration L. Prediction


04/18/23 78

CONCLUSIONS AND FUTURE WORK

• Main results on this research:

Optimal Background Subtraction in GPR for Humanitarian

Demining

• European Radar Conference “EuRAD” (European Microwave week), October 2008


04/18/23 79

CONCLUSIONS AND FUTURE WORK• Shifted and Scaled technique

• Reference Background New criteria• Time and Frequency Domain should equally perform • Accurate removal of antenna crosstalk

• PCA showed reliability in other applications• More efficient implementation online purposes

• FIR filter• A larger number of coefficients may be included in filter

implementation• Key issue Coefficients selection


04/18/23 80

CONCLUSIONS AND FUTURE WORK• 2-sided Linear Prediction

• Tunable algorithm should be tested• Number of A-scans can be selected

• Exponential Averaging should be analyzed after migration• Early tests revealed promising results• Alternative to Linear Prediction • Less dependency on parameters• Low overall energy after Focusing• High Energy-to-Background Ratio after Focusing


04/18/23 81


Detection

THANK YOU

04/18/23 82


Detection

The IRCTR: An International Focus

04/18/23 83


• IRCTR=Research center EEMCS of TUDelft• Main objective Challenging scientific Telecom

research Radar

• Collaboration: Industries, scientific partners, Founding organizations

• Research sectors Program director• Emphasis on internationalization • Research cooperation with Europe, Asia and USA

04/18/23 84


• Millimeter Wave Facilities• Support of Dutch Technology Foundation (STW)• Test and measurement facility for mm waves up to

110 GHz • Network vector analyzers

• Agilent • ABmm

• Anechoic chamber (DUCAT)

Proof of Principle demonstrators at IRCTR

04/18/23 85


• Wireless Communications• Real time OFDM code demonstrator • Transportable Radar for atmospheric remote

sensing• FM-Continuous Wave (FMCW) • Crucial measurement facility

in CESAR


04/18/23 86


• Detection of buried landmines • Since 1997 Dutch Ministry of Defense• Video impulse radar • Stepped frequency radar• Measurement and positioning system


04/18/23 87


• Program director: Alexander G. Yarovoy• Research areas

• Properties of soils • Propagation and scattering of transmission fields• GPR Antennas• Radars• Target classification• UWB technology• Radar signal processing

UWB Technology and Ground Penetrating Radar Group

04/18/23 88


• Radiowave methods Measurement of soil permittivity • Study of short-pulse scattering from dielectric targets

and rough air-ground interface • UWB: bow-tie, spiral, TEM horns, dielectric wedge antenna• UWB applications: Short range radar

UWB telecom Near field sensors


04/18/23 89


• Video Impulse Radars• Stepped Frequency Radars• Target classification


Documents

Humanitarian Demining with Ultra Wide Band Ground Penetrating Radar