a multidisciplinary analysis of frequency domain metal ... · mine detecting dogs (croatia) halo...

VRIJE UNIVERSITEIT BRUSSEL

FACULTY OF APPLIED SCIENCES

DEPARTMENT OF ELECTRONICS AND INFORMATION PROCESSING

A Multidisciplinary Analysis of Frequency Domain Metal Detectors for Humanitarian Demining

Claudio BruschiniBrussels, 20/9/2002 – Public Defence

Thesis submitted to the Faculty of Applied Sciences of the Vrije Universiteit Brussel to obtain the degree of

Doctor in Applied Sciences

2

INTRODUCTION AND THESIS FRAMEWORK

The Landmine Problem – “Classical” Threats & other Threats I • ANTIPERSONNEL BLAST MINES

• AP FRAGMENTATION MINES

PMN (Russia)

PMN2 (Russia)

LI11 (S, D, CH)

minimum-metal

PMR-2A (ex-Yug.)

(Source: EPFL/DeTeC)

3


The Landmine Problem – other Threats II • ANTITANK MINES

(TMRP6, CROATIA)

• FRAGMENTATIONMINES → TRIPWIRES

• UNEXPLODEDORDNANCE(UXO) (KB1,SARAJEVO)

4


The Current Situation in Humanitarian Demining – Solutions

MineDetectingDogs(Croatia)

HALO Trust deminer in Cambodia, Ebinger 420SI

metal detector

Mechanically Assisted Demining

(Sarajevo)

Manual Demining

Demining lane

5


MD Landmine Detection in Humanitarian Demining – the False Alarm Rate Problem

• Humanitarian Demining (HD):very high clearance rate required(~100%)

• Manual methods still often used asprimary procedure → use MD andcheck every alarm

• Vast majority of all deployed minescontain some metal →

MAIN PROBLEM: high False Alarm rate (1:100-1:1000)

(exception: difficult ground conditions)Example of metallic debris

(ruler: 25 cm long)

6


Role of Metal Detectors

• MD still only detector used in the field (apart from dogs) → Continue single-sensor R&D.

• MD are present in nearly every multi-sensor system under research.

→ Research into metal detectors is beneficial for existing systems as well as for future ones.

Current Limitations of Metal Detectors in Humanitarian Demining

• Detection “only”, no discrimination.

• Target and clutter variability -> analysis of realistic and representative (composite) targets andclutter items (Cambodia).

• Soil properties: is “transparent” only to first order -> soil response study (analytical model)

• Lack of scientific information: IPR issues (exception: patents -> http://www.eudem.vub.ac.be/).

7


Aim of this thesis: Metal Detector analysis (theoretical/experimental) and understand how their use in HD could be improved.

Thesis Framework – Main Approaches

• Forward (direct) Problem: use of analytical models

• “Inverse Problem”*: pattern recognition approach (*McFee, 1989)

Bsec(r,t)

DISTANCE (d)

EM Background

CONDUCTIVITY (σ)PERMEABILITY (µ)

SHAPE, SIZE

ORIENTATION

(R1, R2)

(Θ, Φ)

Soil PropertiesBackground Signal

Geometry

Object Properties

Bprim(r,t)

Analytical Models (target)

Analytical Model (soil)

Bsec(r,t)

+

Pattern Recognition

FORWARD PROBLEM

INVERSE PROBLEM

UNKNOWN (target) MEASURED

(exp. data)

NUISANCE

8

MD BASICS

MD Basic Principles: Physics

MD are active, low frequency inductive systems (eddy currents)

Eddy currents are due to time-varying magnetic fields and are basically governed by the law ofinduction (Faraday’s Law):

IPrim(t) → BPrim(r, t) → Jeddy(r, t) → Bsec(r, t) → Isec(t)

PRIMARY COIL

SECONDARY (INDUCED) MAGNETIC

FIELD

CONDUCTIVE OBJECT

PRIMARY MAGNETIC

FIELD

SECONDARY COIL

GROUND

Schematic Primary/Secondary field plot (continuous wave).

9

MD BASICS

General Operating Principles

• Continuous Wave (CW)/Frequency Domain: 1-50 frequencies at 1-100 kHz

■ Multiple Coils: measure change in mutual inductance, M12. Characteristic variables:

Information on target nature contained in amplitude and phase of the received signal.

• Transient (“Pulse”)/Time Domain.

VR

VX

RESISTIVE Component (I)

REACTIVE Component (Q)

ϕ

Vprim

t

V(t)

Vsec(t)

A sec

Asec

ϕ

Time Complex (Impedance) Plane representation

Vsec = VR + i VXPP1

P2

P3

DETECTOR in MOTION

10

MD BASICS

Advanced Developments (Generalities)

Vsec = V(σ, µ, R, d, ...)

→ deliver quantitative information (still missing in HD at present):

• Depth (d), using overlapping coils or signal profile study;

• “Size” (R) (small (=debris?) vs. large);

• Object Type (σ,µ);

• Object Shape (useful for larger objects?).

Solve the inverse electromagnetic induction problem → make some simplifying assumptions

11

EMI MODELLING & ANALYTICAL SOLUTIONS

THEORY: EMI Modelling & Analytical Solutions

Aim

Understanding of the direct (forward) problem

→ Analysis of analytical solutions to some basic problems.

Emphasis on HD operating conditions.

General Form of an Object’s EM Response

• General Form of the Response Parameter:

adimensional quantity [+ permeability µr]

• General Confined Conductor Response Function:Induced currents = set of current patterns (eigencurrents), each ~ simple loop:

→ Following exact solutions are more general in nature!

α σµωljlk=

F ω( ) a cnω2 iωωn+

ω2 ωn2+

--------------------------n 1=

∞

∑+ a cnω

ω iωn–----------------

n 1=

∞

∑+= =

12


Sphere in the Field of a Coaxial Coil

SECONDARYr0

Z

Y

dT

Conductivity σ, Permeability µ, Radius aX

RT

RS

dS

PRIMARY LOOP

LOOP

Induced voltage V(s)= Σ multipole terms:

χn(ka) = Xn(ka) + iYn(ka) = Response Function (complex)

k2a2 = i σµωa2= iα (Response Parameter) (i2=–1)

V s( )2πiµ0Iω

RSRT

dT2 RT

2+( )1 2⁄

--------------------------------

gn dT RT dS RS a;, , ,( ) χn ka( )×

n 1=

∞

∑

×=

STATIC DETECTOR

“Small” sphere (a=1/10×R), or far from the coils (d>>a):only n=1 is relevant (dipole approximation).

13


Dipole Approximation (uniform field)

0.1 0.2 0.3

�1

�0.8

�0.6

�0.4

�0.2

Im��i Dipole� vs. Re ��i Dipole�

Α�0

Α�10

Α�102

Α��

Complex plane representation

0.1 0.2 0.3 0.4 0.5 0.6

�1

�0.5

0.5

1

1.5

2Im�iΧ� vs. Re�iΧ� �Μr�100�

Α�0

Α�6 102

Α�2 103

Α�104

Α�2 104

Α�105

Α��

@f1@f2

1 cm radius:@f1@f2

Steel sphere (µr=100),

1 mm radius:@f1@f2

1 cm radius: @f1@f2

Copper sphere,

1 mm radius:

STATIC DETECTOR

14


Theoretical Analysis – Conclusions

• Possibility of distinguishing between different objects (e.g. ferromagnetic vs. non-ferromagnetic)

• Characteristic phase response → Possibility of identifying some metallic objects

• In addition: phase shift = continuous, monotonically decreasing function of the object size

→ Coarse classification based on target SIZE (actually response parameter).

• Identification of a few likely problems:

• Composite objects with a potentially complex response function;

• Elongated ferromagnetic object: Magnetization not uniform over the object length

→ quite different response curves in the low frequency part

• Orientation dependence of the target’s response

15

EMI GROUND RESPONSE

AIR

Z=0

a

SOILσ, µ1, ε1

X

Z

Y

h

ρφ

I(ω)

VSEC iωµ0πIa J1 x( )[ ]2e2hNλ

0N– µ1λ0N µ0λ1N–

µ1λ0N µ0λ1N+-------------------------------------

x xd

λ0N---------

0

∞∫

iωµ0πIa( )χHS= =

hN h a⁄ , α σµ1ωa2= =0.0001 0.0002 0.0005 0.001 0.002 0.005 0.01

Α30

40

50

60

70

80

90Phase�iΧHS� �Μr�1.001,1.1; h�0.01�

Μr�1.001

Μr�1.1

0.0001 0.001 0.01 0.1 1 10Α0.00001

0.0001

0.001

0.01

0.1

Abs��Re�ΧHS��,�Im�ΧHS� �Μr�1.001,1.1; h�0.01�

Μr�1.001

Μr�1.1

Electromagnetic Induction Ground Response

Soil effects often not sufficiently considered in the existingscientific literature related to HD applications

→ quantitative understanding Magnetic soil

16

EMI GROUND RESPONSE

Frequency Differencing Methods

Already used to reduce soil effects → Why do they work, how well? Analysis via half-space model:

Example #1: → suppress magnetic soil (ex. Förster Minex)

Example #2: → suppress conductive soil

Conclusions

• Quantitative confirmation of the importance of soil effects (for FD systems in particular).

• Role of the soil’s permeability clearly shown: heavily affects the real part of χHS (plateau effect).

• Stressed second order effects (e.g. “magnetic viscosity” = superparamagnetic ground:

→ )

Im∆ Im ω2( )ω1ω2------Im ω1( )–=

Re∆ Re ω2( ) Re ω1( )–=

µ µ0 1 χ0 1iωτ2( )lnτ2 τ1⁄( )ln

------------------------– +

≅ µ∆ µ ω2( ) µ ω1( )– µ0χ0τ2 τ1⁄( )ln

------------------------ω1ω2------ln= =

17

MD RAW DATA ANALYSIS

Introduction

• Commercially available, two frequency, differential system, the Förster Minex 2FD.

• Recording of the detector’s internal signals: (I1,Q1), (I2,Q2), Delta, Audio.

• Different object parameters, laboratory setup, linear scans.

• Analysis of the data in the complex, or impedance, plane.

Scaling effectively removes thelinear dependency on ω of theinduced voltage and makes itpossible to use the Delta signal tosuppress the soil influence.

F1 ω1 I1⋅ ⋅ F2 ω2 I2⋅ ⋅=

18


Typical Signals (linear scan with high density of points)

200 300 400 500 600 700 800 900 1000 1100−10

−5

0

5

10

V (

mV

olt)

Processed Amplitudes vs. Distance along Scan

f1 REALf2 REAL

200 300 400 500 600 700 800 900 1000 1100

−20

−10

0

10

20

V (

mV

olt)

f1 IMAGf2 IMAGDELTA

200 300 400 500 600 700 800 900 1000 11000

100

200

300

400

X (mm)

V (

mV

olt)

AUDIO

Left

LeftPeak

Right

RightPeak

ObjectCentre

−80 −70 −60 −50

−50

−40

−30

−20

−10

0

10

20

f1 REAL (mVolt), f2 REAL (mVolt)

f1 IM

AG

(m

Vol

t), f

2 IM

AG

(m

Vol

t)

RAW data

−5 0 5

−2

0

2

4

6

8

10

12

14

16

18


f1 IM

AG

(m

Vol

t), f

2 IM

AG

(m

Vol

t)

PROCESSED data, around area of interest

f1f2

f1

f2

Left

Right

Left

Right

LP: LeftPeak

ObjectCentre

ObjectCentre

RP: RightPeak

LP1

LP2

RP1

RP2

Raw and processed internal signals plotted in the complex plane

DELTA=c (f1 IMAG – f2 IMAG)

AUDIO = Threshold on DELTA

Typical processed (i.e. filtered and entered) internal and Audio signals

19


Soil Effects / Reference Objects

0

200

400

600

800

0

100

200

300

400

500−4

−3

−2

−1

0

1

2

3

mm (along track coordinate)

2D soil scan: measVUB1/bgnd1, f190, 06042001, h025

mm

mV

−3 −2 −1 0 1 2 3

−2.5

−2

−1.5

−1

−0.5

0

0.5

1

1.5

2

2.5

f10, f20, mV

f190

, f29

0, m

V

MeasVUB7/earth2 (Cambodia soil sample) f1 f2 filtfilt order 75

f1f2

Reference Cylinders perpendicular to scanning direction

↑2D scans. 1D scan over laterite sample.↓

−10 −5 0 5

−15

−10

−5

0

5

10

15

cyl1 PER Test 7.3.1 h=025 f2

f20, mV

f290

, mV

alc1coc1inc1aac1

−50 0 50

−80

−60

−40

−20

0

20

40

60

80

100

cyl2 PER Test 7.3.1 h=025 f2

f20, mV

f290

, mV

alc2coc2inc2aac2

20


Minimum-metal Mine: example of composite object

−10 −5 0 5

−6

−4

−2

0

2

4

6

f1 IM

AG

(m

Vol

t), f

2 IM

AG

(m

Vol

t)

Detonator only

f1f2

−5 0 5

−4

−2

0

2

4

Mine without Detonator

f1f2

−5 0 5

−6

−4

−2

0

2

4

6


f1 IM

AG

(m

Vol

t), f

2 IM

AG

(m

Vol

t)

Live Mine, MD @ 5cm

f1f2

−1 −0.5 0 0.5 1

−0.5

0

0.5

1


Live Mine, MD @ 10cm

f1f2

Response to the detonator cap, to the mine without detonator (striker pin only), and to the live (real) mine at

two different detector heights (all objects flush)

+

Characteristicresponse,but orientation dependent!

21


PMN AP Mine: example of orientation dependence (parallel scans; different orientations)

d

2D PARALLEL scans

Target

−0.5 0 0.5

−1.5

−1

−0.5

0

0.5

1

1.5

f10, normalized

f190

, nor

mal

ized

UB−000−025−1−par1−2D NOT background subtracted, Normalized filtfilt order 45

003004005006007

−0.5 0 0.5−1.5

−1

−0.5

0

0.5

1

1.5

f20, normalized

f290

, nor

mal

ized

hPER

PAR

1D scans at fixed height, differentORIENTATIONS (HORIZ. plane)

Target

−500 0 500

−1500

−1000

−500

0

500

1000

1500

pmnVUB Test 7.4.2 scan 7 f1

f10, mV

f190

, mV

PAR1PAR2PER1PER2

−1000 0 1000

−3000

−2000

−1000

0

1000

2000

3000

pmnVUB Test 7.4.2 scan 7 f2

f10, mV

f190

, mV

PAR1PAR2PER1PER2

22


Metallic Mines (PROM, PMR-2A): typical large ferromagnetic objects

2D response (parallel scans) at f1 and f2 to a PROM mine placed vertically,

passing over the target

2D response (parallel scans) at f1 and f2 to a PMR-2A mine placed

vertically, passing over the target

−100 0 100

−300

−200

−100

0

100

200

300

f10, mV

f190

, mV

ub7/prom−200−050−1−ver NOT background subtracted, NOT normalized filtfilt order 45

011012013014015

−100 −50 0 50 100

−200

−150

−100

−50

0

50

100

150

200

f20, mV

f290

, mV

−150 −100 −50 0 50 100

−250

−200

−150

−100

−50

0

50

100

150

200

250

f10, mV

f190

, mV

7/pmr2−000−300−1−2d−ver NOT background subtracted, NOT normalized filtfilt order 45

011012013014015

−50 0 50

−100

−50

0

50

100

150

f20, mV

f290

, mV

23


Debris Examples, from “daily life” and conflicts – to be differentiated from targets!

24


Debris: examples of categories

deb01-07, horizontal plane; normalized, objects on the surface; PER.

−0.4 −0.2 0 0.2 0.4

−0.8

−0.6

−0.4

−0.2

0

0.2

0.4

0.6

0.8

f10, normalized

f190

, nor

mal

ized

debris−list01−07per NOT background subtracted, Normalized filtfilt order 45

deb01deb02deb03deb04deb05deb06deb07

−0.4 −0.2 0 0.2 0.4

−0.8

−0.6

−0.4

−0.2

0

0.2

0.4

0.6

0.8

f20, normalized

f290

, nor

mal

ized

−0.5 0 0.5

−1.5

−1

−0.5

0

0.5

1

1.5

f10, normalized

f190

, nor

mal

ized

debris−list20−26 background subtracted, Normalized filtfilt order 75

deb20deb21deb22deb23deb24deb25deb26

−0.5 0 0.5

−1.5

−1

−0.5

0

0.5

1

1.5

f20, normalized

f290

, nor

mal

ized

−0.5 0 0.5

−0.8

−0.6

−0.4

−0.2

0

0.2

0.4

0.6

0.8

f10, normalized

f190

, nor

mal

ized

debris−list70−82 NOT background subtracted, Normalized filtfilt order 75

deb80PARdeb80PERdeb81VERdeb82

−0.5 0 0.5

−1

−0.5

0

0.5

1

f20, normalized

f290

, nor

mal

ized

Ferromagnetic, small (nails)

Non-ferromagnetic foils

Shell fragments (ferromagnetic)

25


Conclusions

• Confirmed theoretical elements of the basic models.

• Fluctuations in the soil signal clearly documented in the experimental data.

• Detailed response analysis has allowed to highlight a number of effects:

■ orientation dependencies

■ changes due to axial offsets

■ response of composite objects and their variability.

• Possible to distinguish smaller clutter items from larger objects; some mines have quite characteristicresponses (e.g. PMN)

→ A “qualitative” (coarse) target classification is therefore possible(at least for situations with a sufficient signal to noise (S/N) ratio.)

26

MD FEATURE EXTRACTION & CLASSIFICATION

MD Feature Extraction & Classification

AIM: Extend the previous results providing a quantitative analysis

−100 0 100

−300

−200

−100

0

100

200

300

Re(f1) vs Im(f1) over RoI1

mV

mV

−50 0 50

−200

−150

−100

−50

0

50

100

150

200

Re(f2) vs Im(f2) over RoI2

mV−50 0 50

−150

−100

−50

0

50

100

150

DelRe vs DelIm (all, f2−f1)

mV

−80 −60 −40 −20 0 20 40 60 800

20

40

60

80

100

120

140Histogram for phase of frequency 1

Phase angle

# of

poi

nts

−100 −80 −60 −40 −20 0 20 40 60 80 1000

50

100

150

200Histogram for phase of frequency 2

Phase angle

# of

poi

nts

50

100

150

30

210

60

240

90

270

120

300

150

330

180 0

Phase angle peaks for f1Area: proportional to relative peak frequency

Length: average amplitude in mVLegend: Phase / Peak Frequency / Average Amplitude

17.7 0.15 10941.9 0.13 45.8

50

100

30

210

60

240

90

270

120

300

150

330

180 0

Phase angle peaks for f2Area: proportional to relative peak frequency

Length: average amplitude in mVLegend: Phase / Peak Frequency / Average Amplitude

−25.3 0.19 20−51.7 0.16 93.9

Phase Response and Average Amplitude

27


Classification Opportunities

“Small” vs. “Large” Objects: Features basically derived from the target’s response function →depend on:

Response parameter = permeability × conductivity × (average linear dimension)2

• Phase Angle Peaks and Amplitude Ratio Distribution:

0

0.2

0.4

0.6

0.8

1

−1

−0.5

0

0.5

1−0.5

0

0.5

1

1.5

2

2.5

3

Re(f1), Re(f2); normIm(f1), Im(f2); norm

Am

plitu

de r

atio

debris−list20−26BGND: Highest average amplitude peaks

00.2

0.40.6

0.81

−1

−0.5

0

0.5

1−0.5

0

0.5

1

1.5

2

2.5

3

Re(f1), Re(f2); normIm(f1), Im(f2); norm

Am

plitu

de r

atio

debris−list−Ferro: Highest average amplitude peaksResulting distributions confirm in a quantitative waythe previous qualitative results.

Different object categories (ex. debris) can form clustersin the chosen 3D space.

28


Conclusions / object “size” & type

• Overall Considerations:

■ A combined, simplified user interface has been proposed.

■ Most of the information seems to be contained in the phase response.

■ In some cases other features (ex. amplitude ratio AR, ReRatio) provide additional information.

■ In general only a partial target discrimination seems possible using the other features alone.

■ Important demagnetization effects are clearly apparent for elongated ferromagnetic objects

(Overall conclusions at the end)

29

MD NEAR FIELD IMAGING

MD Near Field Imaging I: Commercial Multisensor System (Hilti Ferroscan)

Ferroscan RV 10 monitor (left) and RS 10 scanner (right)

Original FS images.

flush (+1.6 cm) @ 3 cm (+1.6 cm)flush (+1.6 cm)@ 3 cm (+1.6 cm)

Linear scale.PMN, 60×60 cm

30


MD Near Field Imaging II: Shape Determination via Deconvolution

• Application of image deblurring techniques (deconvolution) to 2D data.

• Simplest approach: assume a linear behaviour:

(M(x,y): measured “image”, R(x,y): real image, P(x,y): detector’s “Point Spread Function” (PSF))

• Idealized scenario which does not take into account the presence of noise η(x,y):

→ use a stabilized version of the inverse filter or alternative filtering techniques (e.g. Wiener).

• Better results were obtained using the Lucy-Richardson (LR) maximum-likelihood algorithm (iterative

nonlinear constrained method):

( = estimate of true image).

M x y,( ) R x y,( ) P x y,( )⊗=

M x y,( ) R x y,( ) P x y,( )⊗ η x y,( )+=

R̂k 1+ x y,( ) R̂k x y,( ) P x– y–,( ) M x y,( )P x y,( ) R̂k x y,( )⊗--------------------------------------------⊗

=

R̂ x y,( )

31


2D Data Taking with a Conventional MD

−100

−50

0

50

100

downtrack coord (mm)

acro

ss tr

ack

coor

d (m

m)

cuthun/cuthA050 f10A 10xDownsampled

500 600 700 800 900 1000 1100

1150

1200

1250

1300

1350

1400

1450

1500

1550

1600

1650

−100

−50

0

50

100

150


acro

ss tr

ack

coor

d (m

m)

cuthun/cuthB050 f10B 10xDownsampled

500 600 700 800 900 1000 1100

1150

1200

1250

1300

1350

1400

1450

1500

1550

1600

1650

20

40

60

80

100

120

140


acro

ss tr

ack

coor

d (m

m)

cuthun/cuthA050,B ABS(f10A+j*f10B) 10xDownsampled

500 600 700 800 900 1000 1100

1150

1200

1250

1300

1350

1400

1450

1500

1550

1600

1650

2

4

6

8

10

12

14


acro

ss tr

ack

coor

d (m

m)

PSF: minech2d/michA010,B ABS(f10A+j*f10B) 10xDownsampled

450 500 550 600 650 700 750 800 850

1450

1500

1550

1600

1650

1700

1750

1800

1850

“Images” of a large object (copper debris, flush, detector at 5 cm). Top: values along the A and B scans. Bottom: absolute value of composed

vector field.

PSF, measured on a point-like object (minimum-metal mine striker pin):

absolute value of composed vector field

d

2D PARALLEL scans

Target

+

32


Deconvolution Results

2

4

6

8

10

12

14

16

18x 10

−4


acro

ss tr

ack

coor

d (m

m)

Pseudoinverse: minech2d/michA030,B ABS(f10A+j*f10B) 10xDownsampled

400 500 600 700 800 900

1450

1500

1550

1600

1650

1700

1750

1800

1850

0

0.2

0.4

0.6

0.8

1

1.2

1.4

x 10−3


acro

ss tr

ack

coor

d (m

m)

Lucy−Richardson Dec.: minech2d/michA030,B ABS(f10A+j*f10B) 10xDownsampled

400 500 600 700 800 900

1450

1500

1550

1600

1650

1700

1750

1800

1850

0.5

1

1.5

2

2.5

3

3.5

x 10−4


acro

ss tr

ack

coor

d (m

m)

Pseudoinverse: minech2d/michA050,B ABS(f10A+j*f10B) 10xDownsampled

400 500 600 700 800 900

1450

1500

1550

1600

1650

1700

1750

1800

1850

0

1

2

3

4x 10

−4


acro

ss tr

ack

coor

d (m

m)


400 500 600 700 800 900

1450

1500

1550

1600

1650

1700

1750

1800

18500

0.5

1

1.5

2

2.5

3

3.5

4

4.5

x 10−5


acro

ss tr

ack

coor

d (m

m)


400 500 600 700 800 900

1450

1500

1550

1600

1650

1700

1750

1800

1850

Examples for a point-like object:

Minimum-metal mine striker pin

@ 3 cm @ 5 cm @ 10 cm

Pseudoinverse filter

Lucy-Richardson algorithm

33


Conclusions / object shape (& depth)

• First high resolution (R=2-3 cm for a flush object) 2D near real-time “images” of shallowly buried

(ferromagnetic) metallic components of mines with relevant metal content (e.g. PMN) and UXO.

Depth penetration improvements seem however necessary for practical applications.

• First deconvolved MD images of minelike objects were also obtained

→ image resolution can be enhanced with deblurring (deconvolution) techniques.→ distinguish point-like from extended or composite objects

• Practical applicability: address

■ PSF choice – depends also on depth!

■ deviations from the linear model. (ferromagnetic objects!)

• Field applicability remains to be demonstrated (resolution, scanning speed, cost).

34


Overall Conclusions / object “size” & type

• Coarse Object Classification Possible:

■ Coarse target classification according to the object size and permeability seems possible

■ Low S/N case: detection still possible but classification gets increasingly difficult → exploit other features.

• Large Metallic Mines/UXO Discrimination relying on their phase response:

■ Results for some large metallic objects (PROM, PMR): possible but attention to composite objects!

■ Initial hope: extend to mines with an average metallic content

→ Might be possible for the PMN, looks more difficult for the PMN2.

• Mine Discrimination:Discriminating mines from clutter or even different mines among themselves looks feasible; in theend it depends however on:

■ Which and how many types of mines are present (a priori knowledge).

■ How much one can rely on stable mine signatures.

■ How representative the debris we had available is, how often multitarget scenarios areencountered.

■ How many clutter objects have a sufficient S/N ratio to allow discrimination.Actual system effectiveness will depend on how much the false alarm rate can be reduced.

35

Hope... life goes on

Sarajevo

Cambodia

a multidisciplinary analysis of frequency domain metal ... · mine detecting dogs (croatia) halo...

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