A Multidisciplinary Analysis of Frequency Domain Metal Detectors for Humanitarian Demining

Transcription

1 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 Bruschini Brussels, 2/9/22 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 The Landmine Problem Classical Threats & other Threats I ANTIPERSONNEL BLAST MINES INTRODUCTION AND THESIS FRAMEWORK LI (S, D, CH) minimum -metal PMN (Russia) PMN2 (Russia) AP FRAGMENTATION MINES PMR-2A (ex-yug.) (Source: EPFL/DeTeC) 2

3 INTRODUCTION AND THESIS FRAMEWORK The Landmine Problem other Threats II ANTITANK MINES (TMRP6, CROATIA) UNEXPLODED ORDNANCE (UXO) SARAJEVO) (KB, FRAGMENTATION MINES TRIPWIRES 3

4 The Current Situation in Humanitarian Demining Solutions INTRODUCTION AND THESIS FRAMEWORK Mine Detecting Dogs (Croatia) Mechanically Assisted Demining Manual Demining HALO Trust deminer in Cambodia, Ebinger 42SI metal detector Demining lane (Sarajevo) 4

5 INTRODUCTION AND THESIS FRAMEWORK MD Landmine Detection in Humanitarian Demining the False Alarm Rate Problem Humanitarian Demining (HD): very high clearance rate required (~%) Manual methods still often used as primary procedure use MD and check every alarm Vast majority of all deployed mines contain some metal MAIN PROBLEM: high False Alarm rate (:-:) (exception: difficult ground conditions) Example of metallic debris (ruler: 25 cm long) 5

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 and clutter items (Cambodia). Soil properties: is transparent only to first order -> soil response study (analytical model) Lack of scientific information: IPR issues (exception: patents -> 6

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, 989) Geometry INVERSE PROBLEM UNKNOWN (target) DISTANCE (d) SHAPE, SIZE (R, R2) ORIENTATION (Θ, Φ) Object Properties CONDUCTIVITY (σ) PERMEABILITY (µ) EM Background Soil Properties Background Signal Pattern Recognition Analytical Models (target) B sec (r,t) + MEASURED (exp. data) B prim (r,t) Analytical Model (soil) FORWARD PROBLEM B sec (r,t) NUISANCE 7

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 of induction (Faraday s Law): I Prim (t) B Prim (r, t) J eddy (r, t) B sec (r, t) I sec (t) PRIMARY COIL SECONDARY COIL PRIMARY MAGNETIC FIELD SECONDARY (INDUCED) MAGNETIC FIELD Schematic Primary/ Secondary field plot (continuous wave). GROUND CONDUCTIVE OBJECT 8

9 MD BASICS General Operating Principles Continuous Wave (CW)/Frequency Domain: -5 frequencies at - khz Multiple Coils: measure change in mutual inductance, M 2. Characteristic variables: V(t) V prim REACTIVE Component (Q) A sec V sec (t) t V X A sec P V sec = V R + i V X P P 2 P 3 ϕ Time Information on target nature contained in amplitude and phase of the received signal. Transient ( Pulse )/Time Domain. ϕ V R Complex (Impedance) Plane representation RESISTIVE Component (I) DETECTOR in MOTION 9

10 MD BASICS Advanced Developments (Generalities) V sec = 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 THEORY: EMI Modelling & Analytical Solutions 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 α = [+ permeability µ r ] σµωl j l k General Confined Conductor Response Function: Induced currents = set of current patterns (eigencurrents), each ~ simple loop: ω 2 F( ω) a c + iωω n ω = + n ω 2 = a + c 2 n ω ω iω n n n = n = Following exact solutions are more general in nature!

12 EMI MODELLING & ANALYTICAL SOLUTIONS Sphere in the Field of a Coaxial Coil STATIC DETECTOR Z Induced voltage V (s) = Σ multipole terms: d T PRIMARY LOOP R T r R S d S SECONDARY LOOP V ( s) R 2πiµ Iω S R T = ( d T + R T ) 2 n = g n ( d T, R T, d S, R S ; a) χ n ( ka) X Conductivity σ, Permeability µ, Radius a Y χ n (ka) = X n (ka) + iy n (ka) = Response Function (complex) k 2 a 2 = i σµωa 2 = iα (Response Parameter) (i 2 = ) Small sphere (a=/ R), or far from the coils (d>>a): only n= is relevant (dipole approximation). 2

13 EMI MODELLING & ANALYTICAL SOLUTIONS Dipole Approximation (uniform field) Complex plane representation Im i Dipole vs. Re i Dipole 2 Im iχ vs. Re iχ Μ r Α STATIC DETECTOR..2.3 Α.5 Α 6 2 Copper sphere, Α.5 Α 2 3 Α 4 Steel sphere (µ mm 2 cm radius:.6 Α Α 2.5 Α 5 Α Α 3

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 4

15 EMI GROUND RESPONSE Electromagnetic Induction Ground Response Soil effects often not sufficiently considered in the existing scientific literature related to HD applications quantitative understanding Magnetic soil Abs Re Χ HS, Im Χ HS Μ r.,.; h. I(ω) a Z AIR.. Μ r. h Z=.. Μ r. SOIL σ, µ, ε X φ ρ Y. Α Phase iχ HS Μ r.,.; h. Μ r. V SEC iωµ πia [ J ( x) ] 2 2h λ N N e µ λ µ λ N N xdx = µ λ N + µ λ N λ = N ( iωµ πia)χ HS Μ r. h N = h a, α = σµ ωa Α 5

16 EMI GROUND RESPONSE Frequency Differencing Methods Already used to reduce soil effects Why do they work, how well? Analysis via half-space model: ω Example #: Im = Im( ω 2 ) Im( ω ) suppress magnetic soil (ex. Förster Minex) ω 2 Example #2: Re = Re( ω 2 ) Re( ω ) 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 χ HS (plateau effect). Stressed second order effects (e.g. magnetic viscosity = superparamagnetic ground: µ µ χ ln( iωτ 2 ) χ ω + ) ln( τ 2 τ ) µ = µ ( ω 2 ) µ ( ω ) = µ ln----- ln( τ 2 τ ) ω 2 6

17 MD RAW DATA ANALYSIS Introduction Commercially available, two frequency, differential system, the Förster Minex 2FD. Recording of the detector s internal signals: (I,Q ), (I 2,Q 2 ), Delta, Audio. Different object parameters, laboratory setup, linear scans. Analysis of the data in the complex, or impedance, plane. F ω I = F 2 ω 2 I 2 Scaling effectively removes the linear dependency on ω of the induced voltage and makes it possible to use the Delta signal to suppress the soil influence. 7

18 MD RAW DATA ANALYSIS Typical Signals (linear scan with high density of points) V (mvolt) V (mvolt) V (mvolt) 5 Processed Amplitudes vs. Distance along Scan f REAL f2 REAL Left Object Right 5 Centre RightPeak f IMAG f2 IMAG DELTA LeftPeak AUDIO X (mm) Typical processed (i.e. filtered and entered) internal and Audio signals f IMAG (mvolt), f2 IMAG (mvolt) Left Right f RAW data ObjectCentre f2 Left Right f REAL (mvolt), f2 REAL (mvolt) f IMAG (mvolt), f2 IMAG (mvolt) PROCESSED data, around area of interest f f2 LP: LeftPeak ObjectCentre RP2 RP: RightPeak RP LP LP2 5 5 f REAL (mvolt), f2 REAL (mvolt) Raw and processed internal signals plotted in the complex plane DELTA=c (f IMAG f2 IMAG) AUDIO = Threshold on DELTA 8

19 MD RAW DATA ANALYSIS Soil Effects / Reference Objects 2D soil scan: measvub/bgnd, f9, 642, h25 Reference Cylinders perpendicular to scanning direction 3 cyl PER Test 7.3. h=25 f2 cyl2 PER Test 7.3. h=25 f mv mm mm (along track coordinate) 2D scans. D scan over laterite sample. 8 f29, mv 5 5 f29, mv MeasVUB7/earth2 (Cambodia soil sample) f f2 filtfilt order 75 f f2 4 6 f9, f29, mv f2, mv alc coc inc aac 8 alc2 coc2 inc2 aac2 5 5 f2, mv f, f2, mv 9

20 MD RAW DATA ANALYSIS Minimum-metal Mine: example of composite object Characteristic response, but orientation dependent! f IMAG (mvolt), f2 IMAG (mvolt) Detonator only f f Mine without Detonator 5 5 f f2 f IMAG (mvolt), f2 IMAG (mvolt) Live Mine, 5cm f f2 5 5 f REAL (mvolt), f2 REAL (mvolt).5.5 Live Mine, cm f f2.5.5 f REAL (mvolt), f2 REAL (mvolt) 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) 2

21 MD RAW DATA ANALYSIS PMN AP Mine: example of orientation dependence (parallel scans; different orientations) UB 25 par 2D NOT background subtracted, Normalized filtfilt order pmnvub Test scan 7 f 3 pmnvub Test scan 7 f f9, normalized f29, normalized f9, mv f9, mv f, normalized f2, normalized f, mv PAR PAR2 PER PER2 2 3 f, mv PAR PAR2 PER PER2 d PER h Target Target PAR 2D PARALLEL scans D scans at fixed height, different ORIENTATIONS (HORIZ. plane) 2

22 MD RAW DATA ANALYSIS Metallic Mines (PROM, PMR-2A): typical large ferromagnetic objects b7/prom 2 5 ver NOT background subtracted, NOT normalized filtfilt order /pmr2 3 2d ver NOT background subtracted, NOT normalized filtfilt order f9, mv f29, mv f9, mv f29, mv f, mv 5 5 f2, mv f, mv 5 5 f2, mv 2D response (parallel scans) at f and f 2 to a PROM mine placed vertically, passing over the target 2D response (parallel scans) at f and f 2 to a PMR-2A mine placed vertically, passing over the target 22

23 MD RAW DATA ANALYSIS Debris Examples, from daily life and conflicts to be differentiated from targets! 23

24 MD RAW DATA ANALYSIS Debris: examples of categories Ferromagnetic, small (nails) debris list 7per NOT background subtracted, Normalized filtfilt order 45 Non-ferromagnetic foils debris list2 26 background subtracted, Normalized filtfilt order 75 deb2 deb2 deb deb23 deb24 deb25 deb deb deb2 deb3 deb4 deb5 deb6 deb7.8.6 f9, normalized.5 f29, normalized f9, normalized.2.2 f29, normalized f, normalized debris list7 82 NOT background subtracted, Normalized filtfilt order f2, normalized deb8par deb8per deb8ver deb f, normalized f2, normalized f9, normalized f29, normalized.5 deb-7, horizontal plane; normalized, objects on the surface; PER f, normalized f2, normalized Shell fragments (ferromagnetic) 24

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 characteristic responses (e.g. PMN) A qualitative (coarse) target classification is therefore possible (at least for situations with a sufficient signal to noise (S/N) ratio.) 25

26 MD Feature Extraction & Classification AIM: Extend the previous results providing a quantitative analysis MD FEATURE EXTRACTION & CLASSIFICATION Re(f) vs Im(f) over RoI Re(f2) vs Im(f2) over RoI2 DelRe vs DelIm (all, f2 f) mv Phase Response and Average Amplitude mv 5 5 mv 5 5 mv 4 Histogram for phase of frequency # of points Phase angle 2 Histogram for phase of frequency # of points Phase angle Phase angle peaks for f Area: proportional to relative peak frequency Length: average amplitude in mv Legend: Phase / Peak Frequency / Average Amplitude Phase angle peaks for f2 Area: proportional to relative peak frequency Length: average amplitude in mv Legend: Phase / Peak Frequency / Average Amplitude 26

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: Resulting distributions confirm in a quantitative way the previous qualitative results. debris list Ferro: Highest average amplitude peaks Different object categories (ex. debris) can form clusters in the chosen 3D space debris list2 26BGND: Highest average amplitude peaks Amplitude ratio.5.5 Amplitude ratio Im(f), Im(f2); norm Re(f), Re(f2); norm Im(f), Im(f2); norm Re(f), Re(f2); norm.8 27

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) 28

29 MD Near Field Imaging I: Commercial Multisensor System (Hilti Ferroscan) MD NEAR FIELD IMAGING Ferroscan RV monitor (left) and RS scanner (right) flush (+.6 cm) Original FS 3 cm (+.6 cm) flush (+.6 3 cm (+.6 cm) PMN, 6 6 cm Linear scale. 29

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): Mxy (, ) = Rxy (, ) Pxy (, ) Mxy (, ) = Rxy (, ) Pxy (, ) + η( x, y) Rˆ k ( Rˆ ( x, y) = estimate of true image). + ( x, y) = Rˆ k( x, y) P( x, y) Mxy (, ) Pxy (, ) Rˆ k( x, y) 3

31 MD NEAR FIELD IMAGING 2D Data Taking with a Conventional MD 5 cuthun/cutha5 fa xdownsampled 5 cuthun/cuthb5 fb xdownsampled across track coord (mm) Target d across track coord (mm) downtrack coord (mm) 2D PARALLEL scans downtrack coord (mm) cuthun/cutha5,b ABS(fA+j*fB) xdownsampled PSF: minech2d/micha,b ABS(fA+j*fB) xdownsampled across track coord (mm) across track coord (mm) downtrack coord (mm) 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 downtrack coord (mm) PSF, measured on a point-like object (minimum-metal mine striker pin): absolute value of composed vector field 3

32 MD NEAR FIELD IMAGING Deconvolution Results 45 Pseudoinverse: minech2d/micha3,b ABS(fA+j*fB) xdownsampled x Pseudoinverse: minech2d/micha5,b ABS(fA+j*fB) xdownsampled x 4 Examples for a point-like object: across track coord (mm) across track coord (mm) Minimum-metal mine striker pin Pseudoinverse filter downtrack coord (mm) downtrack coord (mm).5 Lucy-Richardson algorithm Lucy Richardson Dec.: minech2d/micha3,b ABS(fA+j*fB) xdownsampled 45 x 3.4 Lucy Richardson Dec.: minech2d/micha5,b ABS(fA+j*fB) xdownsampled 45 x 4 4 Lucy Richardson Dec.: minech2d/micha,b ABS(fA+j*fB) xdownsampled 45 x across track coord (mm) across track coord (mm) across track coord (mm) downtrack coord (mm) downtrack coord (mm) downtrack coord 3 5 cm 32

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). 33

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 the end 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 are encountered. 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. 34

35 Hope... life goes on Sarajevo Cambodia 35