a multidisciplinary analysis of frequency domain metal …€¦ · · 2014-05-07a...

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, 21/8/2002 – Private 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 Current Situation in Humanitarian Demining

HALO Trust deminer in Cambodia, checking the ground

with an Ebinger 420SI metal detector

(Top) Example of metallic debris (ruler: 25 cm long):

(Bottom) Chinese Type 72 minimum-metal AP mine (78

mm large, 38 mm high)

THE PROBLEM:

• Presence of Mines and UXO

• Mines flush to a few tens of cmdeep

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

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

• MD still only detector used in thefield (apart from dogs)

• MAIN PROBLEM: high False Alarmrate (1:100-1:1000)

3


Landmine Detection and Current Research Avenues

Large number of diverse scenarios, targets, minefield types (e.g. patterned vs. random)

→ Focus on DETECTION as part of an overall solution

Two main approaches to landmine detection:

• Wide Area vs. Close-In

Different sensor systems can in turn be used individually or in combination:

• Stand Alone vs. Multi-sensor

■ Basic idea behind multi-sensor systems/data fusion:Exploitation of different sensing principles leads to more reliable detection/classification results bycombining different pieces of incomplete or imperfect information.

■ Risk of this approach:Combining insufficiently mature sensors yields an even more complicated problem than pushing individualsensor technologies to their intrinsic physical detection limits→ Continue R&D of single-sensor data processing and pattern recognition techniques.

Recurrent question: “What is a mine” and how much a priori knowledge can be assumed...

4


Role of Metal Detectors and Limitations

• MD still play a considerable role; are present in nearly every multi-sensor system under research.

• Weakness: detection of “only” metal.

• Vast majority of all deployed mines contain some metal – the problem is in the high false alarmrate rather than in detection (exception: difficult ground conditions, e.g. magnetic soils) →

Research into metal detectors is beneficial for existing systems as well as for future ones, and this is wherewe have seen room for improvement.

Metal Detector R&D – is complicated by:

• Lack of scientific information: MD industry is mostly composed of SMEs → IPR issues, littleincentive to publish information (exception: patents, analysed in a separate study [SIG02]).

• Target and clutter variability: Quite large number of target objects in HD (also composite!). Theycan mostly be grouped in a few categories, but the clutter items can basically have any shape.

• Soil properties: A “transparent” soil is only a first order approximation, which gets worse as thetarget object gets less conductive or permeable, smaller or deeper, and as the soil becomes moreconductive, permeable or inhomogeneous.

• Detection much easier than Classification: Classification gets usually worse with decreasing S/N.

5


Aim of this thesis: focus on metal detectors, analyse them from the theoretical and experimental point of view, and understand how their use in HD could be improved.

Thesis Framework – Main Research Directions

• Multidisciplinary approach: Other fields in which similar devices are used with profit have beenanalysed, in particular Geophysics and Non-Destructive Testing (NdT).

• Patent analysis: separate study [SIG02] carried out within the framework of the EUDEM2 survey.

• Use of analytical models: Provide an understanding of the direct, or forward, problem, and havebeen complemented in a semi-quantitative way in the case of elongated ferromagnetic objects.

• Analysis of internal signals: Internal raw (unprocessed) signals have been acquired underlaboratory conditions with a commercial MD → possible to fully characterise an object’s response.

• Use of realistic targets: Apart from reference objects, data taking concentrated on debris collectedduring a previous data taking campaign in Cambodia, and on real mines and their components.

• Pattern recognition approach: Has arisen in a natural way from the analysis of the response curvesin the complex plane and their simplification (feature extraction). It is also motivated by the largenumber of possible clutter shapes and by the clutter to mine ratio (>> 1).

• Near field “imaging”: Generating images with a MD, as a complementary approach.

6

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). Summarising (EM scattering process):

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

Parameters influencing the secondary (induced) magnetic field:

Schematic Primary/Secondary field plot (continuous wave).

PRIMARY COIL

SECONDARY (INDUCED) MAGNETIC

FIELD

CONDUCTIVE OBJECT

PRIMARY MAGNETIC

FIELD

SECONDARY COIL

GROUND

Bsec(r,t)

DISTANCE (d)

EM Background

CONDUCTIVITY (σ)PERMEABILITY (µ)

SHAPE, SIZEORIENTATION(R1, R2)(Θ, Φ)

Soil PropertiesBackground Signal

Geometry Object Properties

Bprim(r,t)

7

MD BASICS

General Operating Principles

• Continuous Wave (CW)/Frequency Domain: one to a few tens of frequencies at 1-100 kHz

■ Single Coil: measure for ex. ∆Z (fixed frequency resonant circuit) or ∆fres (coil is part of oscillating circuit)

■ Multiple Coils (transmit/receive circuits): measure change in mutual inductance, M12 (“Induction-Balance” systems). Try to have M12=0 when no object is present. Characteristic variables:

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

• Transient (“Pulse”)/Time Domain: pulse repetition ~ kHz, exponential decay time ~ 10-several 100µsec. Broadband.

VR=Asec cosϕ

VX=Asec sinϕ

RESISTIVE Component (IN-PHASE)

REACTIVE Component (QUADRATURE-PHASE)

Vsec = VR

ϕ

Vprim

t

V(t)

Vsec(t)

A s

Asec

ϕ

Transmitted and Received signals as a function of time

Complex (Impedance) Plane representation

Vsec = VR

tanϕ= VX/VR

8

MD BASICS

Advanced Developments (Generalities)

Present-day systems are the result of many years’ efforts in increasing sensitivity and autonomy, andmastering background rejection (importance of soil effects!) and ergonomics. The measured signal ishowever rich in information:

Vsec = V(σ,µ,R,d,...)

→ study as well the possibility of delivering quantitative information (still missing in HD at present →needs and opportunities for improvements!):

• Depth (d), using overlapping coils or signal profile study (scanning on a line over the object);

• “Size” (R), possibly with a rough estimate (small (=debris?) vs. large);

• Object Type (σ,µ) (could be useful in situations where there are only a few targets in a given area);

• Object Shape (not easy to reach sufficient resolution; useful for larger objects?).

We are in fact attempting to solve one or more facets of the inverse electromagnetic induction problem.It is usually necessary to make some assumptions to simplify the problem and try to “disentangle” thedependencies on the target’s parameters.

We will mostly take a pattern recognition approach to estimate the target parameters, which are nowadays mostly missing, from measurements.

9

EMI MODELLING & ANALYTICAL SOLUTIONS

EMI Modelling & Analytical Solutions

Aim

Understanding of the direct (forward) problem, which is of great importance for the solution of theinverse problem (Chapters 5 and 6)

→ Analysis of analytical solutions to some basic eddy current forward problems.

■ Analytical solutions exist only for a few basic geometries; analytical models are thus less flexible thannumerical techniques.

■ AN are however an excellent tool to provide general insight into the physics of the scattering processand its dependence on the model’s parameters.

Emphasis on the operating conditions prevailing in HD and on frequency domain systems andtheir phase response in particular.

10


General Form of an Object’s EM Response

• General Form of the Response Parameter:Dimensional analysis to identify which are the parameters affecting a system’s response (ingeneral) → adimensional response parameter α (induction number):

(lj and lk effectively control the volume of the eddy current circulation)

• General Confined Conductor Response Function:Induced currents = set of current patterns (eigencurrents), each with the electrical properties of asimple loop with a purely imaginary pole at ωn (time constant tn=1/ωn). Form a basis into whichany finite sized vortex of eddy currents can be decomposed.Each eigencurrent depends only on the object’s conductivity distribution, not on the primaryfield. The corresponding coupling coefficient cn does however depend on the source field.

Overall Response Function:

Remarks:

■ Non-spherical object: each of the object’s principal axes will support its own system of eigencurrents.

■ Non-uniform primary field = multipole expansion about the principal axes of the target object.

■ “Illumination” from different views → the resulting eddy current patterns change (different modescan be excited, and their excitation strengths vary), in particular for ferromagnetic objects.

α σµωljlk=

F ω( ) a cnω2 iωωn+

ω2 ωn2+

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

∞

∑+ a cnω

ω iωn–----------------

n 1=

∞

∑+= =

11


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:

with χn(ka) = Xn(ka) + iYn(ka) = Response Function (complex) and k2a2 = i σµωa2= iα (Response Parameter) (i2=–1)

V s( ) 2πiµ0IωRSRT

dT2 RT

2+( )1 2⁄

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

a2n 1+

2n n 1+( )-----------------------

Pn1 dT dT

2 RT2+[ ]

1 2⁄⁄

Pn1 dS dS

2 RS2+[ ]

1 2⁄⁄

dT2 RT

2+( )n 2⁄

dS2 RS

2+( )n 1+( ) 2⁄

-------------------------------------------------------------------------------------------------------------- χn ka( )×

n 1=

∞

∑

×=

• “Large” sphere (a=R=0.1): higher ordermultipoles (n=2, n=3) can have substantialcontributions

• “Small” sphere (a=0.01=1/10R): only n=1 isrelevant (dipole approximation). Same is trueif the sphere is far from the coils (d>>a).

12


Dipole Approximation (uniform field)

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13


Summary of Theoretical Analysis

• Details of analytical solutions to some basic eddy current forward problems, with emphasis on theresponse function and the operating conditions prevailing in HD.

• Study of the general form of an object’s EM induction response → confirmation that thesesolutions are more general in nature.Understanding of how they depend on the target’s response parameter and its permeability µr.

• Complemented in a semi-quantitative way for elongated ferromag. objects (ex. short cylinders):Magnetization not uniform over the object length (demagnetization effects) → demagnetizationfactor Nd, which depends mainly on the object shape. In practice the object behaves as if it had anapparent permeability µapp rather than its true permeability µr:

→ quite different response curves in the low frequency part w.r. to a sphere or a transversal cylinder.

• Drawn attention to and described second order effects:

■ Galvanic contributions: should in general not be relevant for the applications of interest to us, with thepossible exception of very large objects in high-conductivity soils.

■ Interaction between the target and the host medium in the form of a full space: Decoupling (additiveapproximation) is often implicitly assumed, but strictly speaking it is not guaranteed a priori.

1µrel--------- 1

µapp----------- Nd µapp⇒–

µrelNdµrel 1+------------------------- χ

ω 0→lim 1+= = =

14


Conclusions

• Possibility of distinguishing between different objects (e.g. ferromagnetic vs. non-ferromagnetic),and of identifying some metallic objects based on their characteristic phase response.

• In addition: the phase shift of the received signal is a continuous, monotonically decreasingfunction of the object size (all other parameters being kept constant)→ idea of a coarse classification based on target size (actually the response parameter), and/or ofdiscriminating large objects by imposing a “phase threshold”.Less ambitious approach than object identification, but should be more robust. Likely to workwhen looking for metallic objects of a certain size (e.g. PMN or PMN2) or UXO, and when theclutter is mostly represented by small and/or poorly conductive metallic objects.

• Identification of a few likely problems:

■ Composite objects with a potentially complex response function;

■ Elongated ferromagnetic object;

■ Orientation dependence of the target’s response.

Some of these results have obviously already been known for a certain time, but to the best of ourknowledge not necessarily in this form by those involved with MD systems applied to HD needs.

→ Added value in having put together this information in a coherent way, with emphasis on HD specificities, and in having moved beyond the simple circuit and sphere approach.

15

EMI GROUND RESPONSE

Electromagnetic Induction Ground Response

Motivation of analysis

Presence and importance of the soil signal for Frequency Domain systems have already been stressed→ important to gain a quantitative understanding of:

• The role of the different parameters – the soil’s EM properties, the metal detector’s loop size andheight.

• Some techniques used to reduce soil effects (e.g. frequency differencing methods).

This will also help in understanding the behaviour of the experimental data (Chapter 5) and in theanalysis of features more robust to background fluctuations (Chapter 6).

16

EMI GROUND RESPONSE

Superparamagnetic Ground (Magnetic Viscosity Effects)

Magnetic permeability is allowed to vary with the frequency ω (magnetic viscosity effect), contrary towhat assumed so far:

(χ0: direct current value of the magnetic susceptibility, τ1, τ2: lower and upper time constant for the superparamagneticground, respectively).

The permeability difference at two frequencies ω1 and ω2 becomes purely real:

→ can be important when using frequency differencing methods!

What happens from the physics point of view is that when the applied field is switched off, the inducedmagnetism does not collapse instantaneously, but with a logarithmic time relationship (in Time Domain).

µ µ0 1 χ0 1iωτ2( )lnτ2 τ1⁄( )ln

------------------------– +

≅

µ∆ µ ω2( ) µ ω1( )– µ0χ0τ2 τ1⁄( )ln

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

17

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= =

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Magnetic soil, height effect

18

EMI GROUND RESPONSE

Frequency Differencing Methods

• Basic idea: the difference signal(s) is/are null when no target object is present, whereas in presenceof a conductive zone the mutual coupling between the transmitter and the receiver coil changesand an anomaly is recorded in the difference channel(s).

• Several different ways of combining a detector’s output at different frequencies to generatedifference signals. Their degree of effectiveness depends on the nature of the background, on thetype of the target and on the operating conditions.

Example #1:

Example #2:

In practice the differencing scheme corresponding to Example #2 is usually employed to suppressthe effect of magnetic soil (for example by the Förster Minex), the one corresponding to Example#2 to suppress the effect of conductive soils [reasoning in terms of response function].

• However: target response (e.g. sphere) can be reduced as well, resulting in a bell-shaped responsecurve peaking at some intermediate value of the response parameter. In addition the magnitude ofthe difference curves decreases with decreasing frequency ratio.

→ Some care has to be applied when using these types of differences, depending on the type of target sought.

Im∆ Im ω2( )ω1ω2------Im ω1( )–=

Re∆ Re ω2( ) Re ω1( )–=

19

EMI GROUND RESPONSE

Soil effects, whose presence we have recognized early on during this work and which are often notsufficiently considered in the existing scientific literature related to HD applications, have beencalculated for different soil permeabilities and (normalized) detector heights.

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 atlow ω). The imaginary part is linear in the response parameter (of the iωσµa2 form).Effects of changes in the detector’s height: in particular on the real part of χHS for magnetic soils.

• These dependencies can also be extrapolated to give an idea of the importance of fluctuations inthe ground’s parameters (inhomogeneities, height profile fluctuations), which will inevitably bepresent in real conditions, and which are also clearly documented in the experimental data.

• Frequency differencing methods to reduce soil effects have also been described, and two of themstudied in more detail. The half-space model has allowed us to understand why they work and toassess in a quantitative way how well they work.

Some of these results have already been known to the MD community from the exp. point of view for acertain time, and partly to the geophysical community as well (but with less emphasis on µsoil).

→ Added value in having joined the two ends, putting together this information in a coherent way with emphasis on humanitarian demining specificities, and with the necessary scientific rigour.

20

MD RAW DATA ANALYSIS

Introduction

• Acquisition and analysis of an extensive amount of metal detector raw data using a commerciallyavailable differential CW system, the Förster Minex 2FD.

• Recording of the detector’s internal signals, i.e. the in-phase and quadrature-phase component ateach frequency, as well as the difference of the two quadrature-phase components and the audiosignal available to the operator.

• Different object parameters, in a laboratory setup with the objects either flush or buried. Linearscans, and in some cases series of parallel scans as well, have been carried out with a high densityof points in the scan direction (Cartesian gantry).

• Analysis of the data in the complex, or impedance, plane (I vs. Q component), similarly to NdTmeasurements. Makes it possible to exploit global object properties rather than only local ones.

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⋅ ⋅=

21


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

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(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

22


2D Soil Scans / Reference Objects and Orientations

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

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−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

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−20

−15

−10

−5

0

5

10

15

20

cyl1 PER Test 7.3.1 h=025 f1

f10, mV

f190

, mV

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100


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f190

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−10

−5

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f290

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−80

−60

−40

−20

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20

40

60

80

100


f20, mV

f290

, mV

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f190

MeasVUB5/msc2−000−025−1−2D−ver−test7.2.012 vs MeasVUB5/msc2−000−025−1−per−test7.2.001 (mean subtracted and norma

f1 first filef1 second file

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0

0.5

f20

f290


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−0.5

0

0.5

f10

f190

MeasVUB5/msc2−000−025−1−2D−ver−test7.2.012 vs MeasVUB5/msc2−000−025−1−par−test7.2.001 (mean subtracted and normal


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−0.6

−0.4

−0.2

0

0.2

0.4

0.6

f20

f290


VER vs. PAR VER vs. PER

VER

PER

VER

PAR

Reference Cylinders perpendicular to scanning direction

Mild Steel cylinder at different orientations

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

23


Minimum-metal Mine

−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

−1 −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8 1

−0.3

−0.2

−0.1

0

0.1

0.2

0.3

f10

f190

measvub6/mide−000−025−1−ver−test7.6.001 vs minech2dreal/mli11b.014 (mean subtracted and normalized)


−2.5 −2 −1.5 −1 −0.5 0 0.5 1 1.5 2

−0.5

0

0.5

f20

f290


Minimum-metal mine: response to a detonator cap (midereal), to the mine without detonator, i.e. striker pin

only (mist), and to the live (real) mine (mich) at two different detector heights (all objects flush)

Minimum-metal mine detonators: replica (mide) vs. original one (midereal); normalized.

24


PMN AP Mine

2D response (parallel scans) at f1 and f2 for pmnVUB, placed at PAR1 (HOR plane). Normalized, scans 3-7

(approaching the target; centre of target between scans 7 and 8), 4 cm increments.

Comparison of the response at f1 and f2 over pmnVUB, for 4 orientations in the HOR plane; scan 7 (above the target).

−0.5 0 0.5

−1.5

−1

−0.5

0

0.5

1

1.5

f10, normalized

f190

, nor

mal

ized

6/pmnVUB−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

−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

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3000

pmnVUB Test 7.4.2 scan 7 f2

f10, mV

f190

, mV

PAR1PAR2PER1PER2

25


Metallic Mines (PROM, PMR-2)

2D response (parallel scans) at f1 and f2 to a PROM mine placed vertically (pronged striker above surface). Scans

11-15 (passing over the target).

2D response (parallel scans) at f1 and f2 to a PMR-2 mine placed vertically (mine above surface). Scans 11-15 (passing

over the target)

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f190

, mV

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

011012013014015

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, mV

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, mV

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011012013014015

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f20, mV

f290

, mV

26


Debris Examples

27


Debris

Response at f1 and f2 to deb01-07 in the horizontal plane; normalized, objects on the surface; PER.

TOP: non-ferromagnetic foils. BOTTOM: shell fragments (ferromagnetic).

−0.4 −0.2 0 0.2 0.4

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, nor

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ized

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

deb01deb02deb03deb04deb05deb06deb07

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, nor

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, nor

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deb20deb21deb22deb23deb24deb25deb26

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, nor

mal

ized

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90, n

orm

aliz

ed

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

28


In this chapter we have dealt mostly with qualitative aspects of the phase response, looking atsignal trajectories in the complex plane.

Conclusions

• A number of theoretical elements of the basic models we looked at have been confirmed, in particular thetrends in the phase response with increasing object size and/or conductivity, the differencebetween ferromagnetic and non-ferromagnetic objects, and important demagnetization effects.Fluctuations in the soil signal are also clearly documented in the experimental data.

• Where we see particular added value for the scientific community is in the detailed responseanalysis, which has allowed to highlight a number of effects such as orientation dependencies orchanges due to axial offsets (elongated ferromagnetic targets). Subtle effects have also beendocumented: response of composite objects and their variability, or of different versions of a samemine.

• We have also shown that it is possible to distinguish smaller clutter items from larger objects, andthat some mines have quite characteristic responses (e.g. PMN). A “qualitative” (coarse) targetclassification is therefore possible, at least for situations with a sufficient signal to noise (S/N) ratio.

Although some of these results were already known to the MD community, their diffusion has rarely happened, to the best of our knowledge, in a public document and in a coherent way, with the

necessary scientific rigour.

29

MD FEATURE EXTRACTION & CLASSIFICATION

MD Feature Extraction & Classification

Aim

• Extend the previous results providing a quantitative analysis, in order to:

■ Provide complementary information to the “complex plane” user interface;

■ Address situations in which an object by object analysis by a human operator is not possible(automated interpretation), having for example vehicle based systems in mind,

■ Study classification opportunities.

Preprocessing Steps

• Eliminate offsets and Filter (lowpass);

• Region of Interest (RoI) definition: define signal edges.

■ (Noteworthy) Low Real S/N case: apply a polynomial FIR smoothing filter (Savitzky-Golay filter) oforder PolyOrder and width FrameSize samples to the sum of the absolute values of f1REAL, f2REALand DeltaImag. Select the 2 peaks with highest amplitude. The sum has been taken in order toincrease the S/N ratio.

LowRe S N⁄( ) Re f1( ) Re f2( ) ∆Imag+ +=

30


Feature Definition & Extraction – Phase Response and Average Amplitude I

−50 0 50

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−50

0

50

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Re(f1) vs Im(f1) over RoI1

mV

mV

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0

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300

400

500


mV−100 0 100

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DelRe vs DelIm (all, f2−f1)

mV

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

20

40

60

80

100Histogram for phase of frequency 1

Phase angle

# of

poi

nts

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

50

100

150


Phase angle#

of p

oint

s

0.1

0.2

0.3

30

210

60

240

90

270

120

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150

330

180 0

Phase angle peaks (appearance freq.) for f1Normalised by the total # of histogram entries

Fixed axes, max value= 0.25

63.9 0.12−14.3 0.08−33.8 0.026 36.5 0.023

50

100

30

210

60

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90

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180 0

Average amplitudes of phase angle peaks for f1

83.377.740.327.4

0.1

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0.3

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180 0

Phase angle peaks (appearance freq.) for f2Normalised by the total # of histogram entries

Fixed axes, max value= 0.25

−49.7 0.25

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Average amplitudes of phase angle peaks for f2

264

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Phase angle peaks for f1Area: proportional to relative peak frequency

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

63.9 0.12 27.4−14.3 0.08 77.7−33.8 0.026 83.3 36.5 0.023 40.3

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−49.7 0.25 264

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Phase angle peaks for DeltaZ (frequency differencing)Area: proportional to relative peak frequency


−59.9 0.22 188−83.8 0.081 11.1

31


• Phase Response and Average Amplitude II

−100 0 100

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mV−50 0 50

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DelRe vs DelIm (all, f2−f1)

mV

−80 −60 −40 −20 0 20 40 60 800

20

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120


Phase angle

# of

poi

nts

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# of

poi

nts

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17.7 0.15 10941.9 0.13 45.8

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−25.3 0.19 20−51.7 0.16 93.9

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180 0

Phase angle peaks for DeltaZ (frequency differencing)Area: proportional to relative peak frequency


84 0.2 9.42 64 0.2 109−87.9 0.027 5.93

32


Other Features

• Absolute Amplitude Ratio: Calculated in two ways (AR1, AR2), and their average AR:

• Real Part Ratio: Calculated as the ratio of their peak to peak value at the two frequencies (realcomponent quite unaffected by the background):

• L/R (Simple Circuit Model):

Using frequency differencing techniques:

• DeltaZ (Differential Signal):

AR1V1

V2

--------- , Vi Vi k( )k 1=

totSamples

∑

totSamples( )⁄= = AR2max1 V1( ) max2 V1( )+max1 V2( ) max2 V2( )+---------------------------------------------------------------= AR AR1 AR2+

2----------------------------=

ReRatiomax Re f1( )( ) min Re f1( )( )–max Re f2( )( ) min Re f2( )( )–---------------------------------------------------------------------=

L R⁄ fi

1ωi-----

Im fi( )Re fi( )---------------

– 1ωi----- ϕi , i=1,2tan–= =

L R ∆Weighted⁄ 1ω1------DeltaReWeighted

DeltaIm----------------------------------------------- 1

ω1------

Re f2( ) freqRatio⁄ Re f1( )–( )Im f2( ) Im f1( )–

------------------------------------------------------------------------= =

Real DeltaZ( ) Re f2( ) Re f1( ) , Imag DeltaZ( )– DeltaIm Im f2( ) Im f1( )–= = =

33


Classification Opportunities

• “Small” vs. “Large” Objects: Features basically derived from the target’s response function →depend on its response parameter = permeability × conductivity × (average linear dimension)2.Refer to an object size using terms such as “small” and “large”; strictly speaking we are howeverdiscriminating on the full response parameter rather than on its physical size only.

• Phase Angle Peaks and Amplitude Ratio Distribution:

0

0.2

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1

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

Am

plitu

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atio

debris−list20−26BGND: Highest average amplitude peaks

00.2

0.40.6

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plitu

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atio

debris−list−Ferro: Highest average amplitude peaksThe resulting distributions of the phase angle peaks only confirmin a quantitative way the qualitative results detailed in theprevious chapter.

In addition the complete distributions detail how different objectcategories, in particular debris, can form clusters in the chosen

3Dspace.

34


Amplitude Ratio and Real Part Ratio Distribution

Average amplitude ratio AR vs. ReRatio. Left: all non-ferromag. debris, Right: all ferromag. debris (+ mines/UXO superposed).

0 0.5 1 1.5 2 2.5 30

0.1

0.2

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0.6

0.7

0.8

0.9

1debris−list−NonFerro: Average Amplitude Ratio vs. ReRatio

Re(f1)/Re(f2)

A(f

1)/A

(f2)

12

3

456789

1011

1213

1415

16

1718

19

2021

22

2324

25

2627

2829

3031

deb100−104

deb107

deb77

deb79

deb78

deb16

deb14

deb20

deb40

deb21−26

deb90

deb41

0 0.5 1 1.5 2 2.50

0.5

1

1.5

2

debris−list−Ferro: Average Amplitude Ratio vs. ReRatio

Re(f1)/Re(f2)

A(f

1)/A

(f2)

1

2

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4445

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48

495051

52

5354

5556

PMNVUB

PROM VER

PMR VER

bul1 VER

mist miviet

PMR2, bul1 PAR, VER

PROM VER

35


Conclusions

• Overall Considerations:

■ The peak finding algorithm in particular seems to be working well.

■ A combined, simplified user interface has been proposed.

■ Most of the information seems to be contained in the phase response. There are however cases inwhich the use of the amplitude ratio AR, and of the ReRatio, provides additional information.

■ In general only a partial target discrimination seems possible using the (AR,ReRatio) values alone.

■ The models’ predictions on the behaviour of AR and ReRatio are confirmed by the experimental data.Important demagnetization effects are apparent for elongated ferromagnetic objects

• Coarse Object Classification Possible:

■ A coarse target classification according to the object size and permeability (ferromagnetic or not),seems indeed to be possible, at least for scenarios with a sufficient S/N ratio.

■ Low S/N case: detection is still possible but classification gets increasingly difficult. It should still be possibleto exploit features such as ReRatio, L/R|∆Weighted and DeltaZ albeit with reduced discrimination capabilities.

• Large Metallic Mines/UXO Discrimination:

■ Initial idea: discriminate large targets relying on their phase response. Results for some large metallicobjects (PROM, PMR, bul1) confirm that this is possible [but attention to composite objects!].

■ It can be necessary to complement the phase response with AR to resolve ambiguities (e.g. PROM).

36


Conclusions (cont.)■ Initial hope: extend this discrimination approach also to mines with an average metallic content (e.g. PMN,PMN2). This might be possible for the PMN if the signature from the ring is reasonably stable. It looks moredifficult for the PMN2, which is composed of about 10 pieces of various sizes → mostly ferromagnetic response,but not equivalent to that of a single larger target.

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

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

■ How much one can rely on stable mine signatures. This influences the tolerance “windows” whichwill have to be applied around the known targets of interest.We can distinguish two aspects here: differences at the mine component level, and differences in thebehaviour of a given type of object.

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

■ How many clutter objects have a sufficient S/N ratio to allow discrimination.Indeed, even provided that one can discriminate mines from clutter, the actual system effectivenesswill depend on how much the false alarm rate can be reduced, i.e. how many times a clutter item has asufficient S/N to be identified as such.

37

MD NEAR FIELD IMAGING

MD Near Field Imaging

• Complementary approach: provide information on the object’s size and shape, which could beuseful in discriminating in certain circumstances mines and/or UXO from clutter. “Conventional”metal detectors can be used to generate images of buried metallic objects.

• Justification: Sensors are operated in the near field rather than in the far field as for “classical”imaging applications→ Coupling is by evanescent waves as well as propagating waves, and geometrical properties canand do play a primary role. In the near field the resolution depends on the degree to which theevanescent waves can actually be measured. The ultimate resolution is determined by the quasi-static nature of the EM fields that both illuminate the target and are scattered by it.→ It makes sense to measure the induced magnetic field, or its gradient, scanning a single sensoror using arrays, and build a 2D image of the measured signal amplitudes.

• Applications: two portable high resolution applications:

■ 1) A commercial multisensor systems designed for NdT applications in civil engineering,

■ 2) Application of image deblurring techniques (deconvolution) to bidimensional data obtained with acommercially available sensor, the Förster Minex metal detector.

38


Commercial Multisensor System (Hilti Ferroscan)

Ferroscan RV 10 monitor (left) and RS 10 scanner (right) Ferroscan scanning procedure, lengths in cm

PMN. Left: original FS images. Right: linear scale. Depth: 1st and 3rd: flush (+1.6cm), 2nd and 4th: 3 cm (+1.6cm). Image size 60×60 cm.

39


Shape Determination via Deconvolution

• Simplest approach: assume a linear behaviour, whereby in the spatial domain:

(M(x,y): measured “image”, R(x,y): real image, P(x,y): detector’s impulse response, or “PointSpread Function” (PSF))

• Convolution operation in the frequency domain → (element-by-element matrix) multiplication ofthe corresponding Fourier transforms :

Idealized scenario which does not take into account the presence of noise η(x,y), usually assumedto be additive:

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

• Better results were however obtained using the Lucy-Richardson (LR) maximum-likelihood algorithm,a member of the family of iterative nonlinear constrained methods:

( is our estimate of the true image).

M x y,( ) R x y,( ) P x y,( )⊗=

ℑ

ℑ M( ) ℑ R( ) ℑ P( ) R⇒⋅ ℑ 1– ℑ M( )ℑ P( )--------------

= =

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,( )

40


2D Data Taking with a Conventional MD

−100

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downtrack coord (mm)

acro

ss tr

ack

coor

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m)

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“Images” of a large object (copper debris, flush, detector at 5 cm) using the f1REAL signal. Top: values along the A and B scans. Bottom: composed vector

field (right) and its absolute value (left).

PSF, measured on a point-like object (minimum-metal mine, striker pin only (mist)): composed vector field (top)

and its absolute value (bottom)

41


Deconvolution Results

2

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ack

coor

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m)

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Examples of deconvolved images for a point-like object, a minimum-metal mine buried at 3, 5 and 10 cm (1st, 2nd and 3rd column), striker pin only (mist), detector at 2-3 cm. Upper

row: pseudoinverse filter, Lower row: Lucy-Richardson maximum likelihood algorithm.

42


Conclusions

• We have shown, to the best of our knowledge, the first high resolution (R=2-3 cm for a flush object)2D near real-time “images” of shallowly buried (ferromagnetic) metallic components of mineswith relevant metal content (e.g. PMN) and UXO. Depth penetration improvements seem howevernecessary for practical applications.

• First deconvolved MD images of minelike objects were also obtained, using 2D data taken with acommercial detector designed for demining applications, demonstrating that image resolution canbe enhanced with deblurring (deconvolution) techniques. The resulting resolution could at present be sufficient to distinguish point-like from extended orcomposite objects, which could be useful in a number of scenarios. Improvements can come froma finer sampling of the PSF in the across track direction for example.

• Tuning of the deconvolution algorithms is however not always straightforward, in particular in thepresence of noise, and the resulting images can present artifacts.

• Practical applicability will also have to address two basic issues, namely the PSF choice anddeviations from the linear model, which are likely for ferromagnetic objects.

• Whether they will be practically applicable in the field, from the point of view of the resultingresolution, scanning speed and cost, remains to be demonstrated.

a multidisciplinary analysis of frequency domain metal …€¦ · · 2014-05-07a...

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