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
INTRODUCTION AND THESIS FRAMEWORK
The Landmine Problem – other Threats II • ANTITANK MINES
(TMRP6, CROATIA)
• FRAGMENTATIONMINES → TRIPWIRES
• UNEXPLODEDORDNANCE(UXO) (KB1,SARAJEVO)
4
INTRODUCTION AND THESIS FRAMEWORK
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
INTRODUCTION AND THESIS FRAMEWORK
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
INTRODUCTION AND THESIS FRAMEWORK
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
INTRODUCTION AND THESIS FRAMEWORK
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
EMI MODELLING & ANALYTICAL SOLUTIONS
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
EMI MODELLING & ANALYTICAL SOLUTIONS
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
EMI MODELLING & ANALYTICAL SOLUTIONS
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
MD RAW DATA ANALYSIS
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 REAL (mVolt), f2 REAL (mVolt)
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
MD RAW DATA ANALYSIS
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
MD RAW DATA ANALYSIS
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 REAL (mVolt), f2 REAL (mVolt)
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
f1 REAL (mVolt), f2 REAL (mVolt)
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
MD RAW DATA ANALYSIS
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
MD RAW DATA ANALYSIS
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
MD RAW DATA ANALYSIS
Debris Examples, from “daily life” and conflicts – to be differentiated from targets!
24
MD RAW DATA ANALYSIS
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
MD RAW DATA ANALYSIS
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
MD FEATURE EXTRACTION & CLASSIFICATION
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
MD FEATURE EXTRACTION & CLASSIFICATION
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
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
MD NEAR FIELD IMAGING
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
downtrack coord (mm)
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
downtrack coord (mm)
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
downtrack coord (mm)
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
MD NEAR FIELD IMAGING
Deconvolution Results
2
4
6
8
10
12
14
16
18x 10
−4
downtrack coord (mm)
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
downtrack coord (mm)
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
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1850
0.5
1
1.5
2
2.5
3
3.5
x 10−4
downtrack coord (mm)
acro
ss tr
ack
coor
d (m
m)
Pseudoinverse: minech2d/michA050,B ABS(f10A+j*f10B) 10xDownsampled
400 500 600 700 800 900
1450
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1550
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0
1
2
3
4x 10
−4
downtrack coord (mm)
acro
ss tr
ack
coor
d (m
m)
Lucy−Richardson Dec.: minech2d/michA050,B ABS(f10A+j*f10B) 10xDownsampled
400 500 600 700 800 900
1450
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1550
1600
1650
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18500
0.5
1
1.5
2
2.5
3
3.5
4
4.5
x 10−5
downtrack coord (mm)
acro
ss tr
ack
coor
d (m
m)
Lucy−Richardson Dec.: minech2d/michA101,B ABS(f10A+j*f10B) 10xDownsampled
400 500 600 700 800 900
1450
1500
1550
1600
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1700
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1850
Examples for a point-like object:
Minimum-metal mine striker pin
@ 3 cm @ 5 cm @ 10 cm
Pseudoinverse filter
Lucy-Richardson algorithm
33
MD NEAR FIELD IMAGING
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
MD FEATURE EXTRACTION & CLASSIFICATION
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