Humanitarian demining: sensor technology status

Humanitarian demining: sensor technology status and signal processing aspects Marc Acheroy and Idesbald van den Bosch†

Abstract This paper presents the status of sensor technology, including operational characteristics without aiming at being exhaustive. Signal processing aspects and important lessons on data fusion are also discussed briefly. The detection is considered as a global process in which the outputs of the sensors, considered as skilled specialists, are integrated in a fusion operation.

1 Introduction A huge amount of antipersonnel (AP) mines are polluting the environment in about 60 countries. Thanks to the Ottawa Convention, mine clearing operations have been organized in a more controlled and effective way. Nevertheless, mine clearance remains a very slow process. It is estimated that, on average, a deminer is clearing an area of 10 m2 every working day if he is using conventional tools, i.e. metal detectors and prodders. To give an idea of the extent of the problem, in Cambodia only approximately 260 km 2 have been cleared during the last ten years. Therefore, humanitarian mine clearance operations must be understood and designed correctly, keeping in mind that their main goal is to provide efficient aid to innocent people, who may be severely injured by this dreadful pollution. Furthermore, the analysis of actual demining campaigns not only reveals the far too long time needed to clear polluted terrain, but also brings to the fore a far too large false alarm rate, the threat of plastic mines (which are difficult to detect by classical means i.e. by metal detectors), and the large variety of mine clearance scenarios, depending on the country, the region, the climate and the place of the pollution (houses in villages, roads, agricultural fields, etc). In order to understand the incurred risks, let us consider the event A : “occurrence of an alarm” in a given position x y (obviously linked to the use of a particular detection system) and two contradictory events M: “the presence of a mine” and M: “the absence of mine”. The probability p A x y of the occurrence of an alarm in x y can then be expressed as follows:

pA x y

pM x y pA

M

x y p M x y pA

M

x y

Therefore, the important parameters, which characterize the mine detection problem, are the mine occurrence probability, the detection probability of a given material and the false alarm probability of a given material [1]:

The mine occurrence probability, pM x y 1 pM x y , in a given position x y of a minefield expresses the local mine density of that minefield as well. Obviously, it is impossible to control

E-mail: [email protected] – Royal Military Academy (RMA) – Avenue de la Renaissance 30, B-1000 Brussels - Belgium – Website: http://www.sic.rma.ac.be † E-mail: [email protected] – Université catholique de Louvain (UCL) – Microwaves UCL – 3 Place du Levant - B-1348 Louvain-La-Neuve - Belgium – Website: http://www.emic.ucl.ac.be

this parameter because it depends on the reality of the terrain. Nevertheless, this parameter is very important for assessing the probability of an alarm in a given location of the minefield.

The detection probability, pA M x y , is the probability of having an alarm in a given position x y of a minefield for a given detection material, if there is a mine in that position. This probability gives indirectly a measure of the non-detection probability of that material as well.

The probability of false alarm, pA M x y , is the probability of having an alarm, for a given material, in a given location x y if there is no mine in that location.

The two latter definitions are extremely important to understand the humanitarian demining problem and for designing demining systems.

Obviously, the detection probability pA M x y should be as close as possible to one. Evaluating the detection probability also amounts to evaluating the risk p A M x y of the occurrence of a mine that has not been detected, since p A M x y 1 pA M x y . This risk is concerned with human preservation and is therefore of the utmost importance. No such risk is acceptable and it is therefore an absolute requirement that a demining system should decrease the probability of such a risk to the lowest upper bound possible. As a matter of fact, it is imperative to evaluate the detection probability when optimizing the performances of a system. However, the detection probability pA M x y , as it is defined above, assumes that a mine is present in the considered position x y . Since, during organized trials, the position of the mines is well known, the condition of the presence of a mine in the given position x y where the performances of a system must be evaluated is always realized. This latter remark is of particular importance because it justifies the organization of trials and the construction of models, to be validated by trials, in order to evaluate the detection probabilities.

Besides indirectly saving human lives by decreasing the false alarm risk p A M x y thanks to the acceleration of the demining operations, the false alarm risk is also a question of cost. Indeed, a demining method which minimizes the false alarm rate results in an acceleration of the demining operations, which in turn results in spending less money. Therefore, any demining operation enhancement must result in the highest possible detection probability pA M x y (close to one) and in the smallest possible false alarm rate pA M x y , all that at the lowest price. Generally, it is accepted that the most efficient way for increasing the detection probability while minimizing the false alarm rate consists in using several complementary sensors in parallel and in fusing the information collected by these sensors. Indeed, if the event A: “absence of an alarm”, is equivalent to the event A 1 A2 A3 AN : “absence of an alarm for all sensors and associated signal processing”, then the overall detection probability is described by: pA M x y 1 p A M x y 1 p A1 M x y p A2 MA1 x y p A3 MA1 A2 x y Obviously, the latter expression shows that the detection probability increases with the number of different sensors. Moreover if the selected sensors are independent1: pA

M

x y

1 p A

M

x y

1 p A1

M

x y p A2

M

x y p A3

M

x y

In this case, maximizing the overall detection probability pA M x y of a set of independent sensors clearly amounts to the same as maximizing the detection capabilities of each sensor individually (e.g. p Ak M x y 1 p x y for the kth sensor), by optimizing separately the design of each sensor and of the associated

Ak M

signal processing . This justifies the use of several complementary sensors and of data fusion techniques to increase the detection probability. Among the most cited sensors one finds the metal detectors, the radars and the infrared sensors. A lot more are presented in section 2. Finally, the false alarm risk, i.e. the probability of having an alarm if there is no mine, cannot be as easily evaluated as the detection probability because of the use of data fusion methods which favor the manual or 1 The independence is a particular case of the complementarity: two independent sensors are complementary, but the contrary is not necessarily true.

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automatic cancellation of false alarms. Furthermore, it is very difficult to evaluate the risk of false alarm because it is very difficult to define in a general way what is not a mine. In this context, it should be particularly inappropriate that a demining system, whatever it may be, makes a decision instead of the final user whose own physical security is involved. Therefore, a well designed system should help the user in the decision making, not by replacing him, but by implementing efficient data fusion methods. For this purpose, methods which are able to deal with uncertainty by making proposals including the doubt to the user seem to be promising. This paper will only consider close-in detection in two steps. The first step, sketched in section 2, will consist in describing potential technologies for mines detection and, when appropriate, their associated processing. Some of these technologies are already in use (e.g. prodders and metal detectors). Some will be fielded very soon (e.g. the ground penetrating radar (GPR)). Others will only appear in the field in the long term because they are not mature and still need a lot of developments. The second step is sketched out in section 3 and consists in fusing the information produced by the different sensors with their dedicated processing tools.

2 Sensor description In this section, it will be tried to describe succinctly sensors of different types without claiming exhaustivity. We will subdivide the description into four categories: 1. Prodders, seismic and acoustic sensors 2. Electromagnetic sensors (Metal detector, GPR, Microwave radiometer, Electrical Impedance Tomography, Electrography, Imaging with handheld sensors) 3. Electro-optic sensors (visible, IR, multispectral, hyperspectral, LIDAR) 4. Explosive detectors (NQR, X-rays, Neutron activation, Biosensors, Trace explosive detection). The first three sensor categories are not able to discriminate between an explosive material and any material with the same electro-magnetic, thermal and/or optical properties, but often offer good localisation capabilities as well as 2-D and even 3-D imaging capabilities for some of them. The last category aims at detecting explosive material, often offers poor localisation capabilities and lacks for spatial resolution as well as for 2-D or 3-D capabilities. Most of the time these sensors require a long integration time, which makes them more suitable as confirmation devices. In the latter case, they are used in combination with sensors of the first three categories. For each of these categories, a table will describe for each sensor its status of maturity (“R&D”, “In Development, “In Use”), its cost and clearance speed (“Very Low”, “Low”, “Low to Medium”, “Medium”, “Medium to High”, “High”, “Very High”), and its effectiveness (“Unknown”, “Low”, “Low to Medium”, “Medium”, “Medium to High”, “High”).

2.1 Prodders, seismic and acoustic sensors Manual prodders can also be enhanced by the addition of an ultrasonic sensor at the prodding extremity. This so-called “Smart Prodder” allows to have a better guess of encountered objects and to exercise less physical pressure on them (in this way, decreasing the risk of accidents). Seismic devices are listening to the response of the ground to a shock applied at the ground surface from a safe position. In the case of an acoustic sensor, an ultra-sonic wave is sent into the ground and the backscattering of this wave by buried objects and the environment is analysed. The application of this method is limited to

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soil with a very high level of moisture or in the water (paddy fields) for which the propagation of ultrasonic waves is possible. This latter characteristic makes ultra-sonic sensors complementary to the ground penetrating radar (GPR), the performances of which are optimal when the moisture level of the soil is low. The operational characteristics of these sensors are described in table 1.

2.2 Electromagnetic sensors 2.2.1 Metal detectors There exist three families of metal detector: the first one, based on electromagnetic induction (EMI), sends a primary magnetic signal in the ground in an emitting phase during which it creates eddy currents in the buried metallic objects which in turn create a secondary magnetic field. During a listening phase, the emission is stopped and the system listens to the secondary magnetic field which induces eddy currents in the coils of the detector. These currents are characteristic of the buried metallic objects and of the soil. There exist two types of EMI devices: the first one sends a magnetic pulse, the second one a continuous wave at different frequencies in a stepped frequency mode. In the second family, the detector, called magnetometer, mesures the local perturbations of the earth magnetic field. In the third one, the detector, called gradiometer, measures the magnetic field gradient in a given direction depending of the sensor configuration. The most used family of detectors is the first one, based on EMI. Surprisingly, the metal detector of the first family (which is the most common detector), considered as an imaging device, can also provide very useful information on the shape of metallic pieces included in mines. Unfortunately, the point spread function (PSF) of a metal detector is a function of the depth (see Fig. 1) and of the nature of the buried metallic object (eddy currents are different in a close and in an open circuit) and the image formation process is non linear. However, the in depth modelling of the metal detector behavior as a function of the type of buried object by the RMA [4] has shown that it is possible to derive the depth of a buried object from the original data and thus to derive the corresponding PSF to allow a correct deconvolution (see Fig. 2, 3 and 4). Further information on the symmetry properties of the buried metallic objects can easily be extracted. This subject is still under investigation. This interesting consideration shows that the metal detector, known as a cheap mine detection system, remains a promising device.

Figure 1: Metal detector — deconvolution of a metallic ball at 5.5 cm (top) and 8.0 cm (bottom) — Left images are the original ones, right images the deconvolved ones.

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Figure 2: Metal detector — deconvolution of a straight metallic wire.

Figure 3: Metal detector — deconvolution of a metallic open loop.

Figure 4: Metal detector — deconvolution of a metallic closed loop.

2.2.2 The ground penetrating radar (GPR) Useful definitions to understand what follows have to be given first. An A-scan is a one-dimensional signal taken perpendicular to the ground surface and is the basic echo signal produced by a GPR. A B-scan is a two-dimensional signal resulting from a collection of adjacent A-scans along a straight line horizontal to the ground surface. A C-scan is a two-dimensional horizontal slice (parallel to the ground surface) in a set of adjacent B-scans. The GPR includes an emitting system (transmitter) and a receiving system (receiver). The transmitter emits a pulse wave or a continuous wave at given frequencies. The receiver collects the waves backscattered by discontinuities in permittivity. Discontinuities can be provoked by buried objects like landmines (useful signal) but also by natural discontinuities of the soil (clutter). This means also that a GPR is able to detect plastic objects buried in the ground. There are mainly two important types of GPR depending the emitted signal: the first one sends a short pulse into the ground (Ultra wideband pulse GPR), the second one sends a continuous wave in a stepped frequency mode. The advantage of the second type is that it provides directly the Fourier transform of the received signal and that more energy can be sent into the ground at a given frequency. The performances and design of advanced microwave technologies such as ground penetrating radars or passive radiometers strongly depend upon the electro-magnetic ε µ σ parameters of the medium of propagation. Their values—which are frequency-dependent—are governed by geophysical parameters such as soil water content, type, texture, and structure [7]. As these factors are strongly space and time-dependent, it is of paramount importance to measure these parameters locally [12], without perturbing the soil. First

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laboratory measurements of the complex permittivity of various soils, both sandy and silty, in the 2–18 GHz frequency range, as a function of water content [20], were made using a transmission method [8]. As this measurement method perturbs the soil under consideration, however, the recently developed approach of [10, 11] is used, which is based on an off-ground monostatic SFCW radar enabling nondestructive characterization of the EM parameters of the soil under consideration. Research is currently extending the above approach for extracting the signature of an inhomogeneity (perfectly conducting or dielectric) embedded in the multilayered medium [22]. In this situation EM wave propagation and scattering phenomena are very complex, yet measured and computed target’s radar signature agree relatively well. Measurements and simulations are done in the frequency domain, however a Fourier transform allows one to calculate the measured and simulated time-domain fields as if they were due to a time-domain pulse. Current GPRs are working in a frequency range comprised approximately between 0.4 and 6.0 GHz. Fig. 5 shows a sample A-scan of a PMN mine in loam. Fig. 6 presents two B-scans of a PMN mine respectively in loam and in sand and a C-scan of a PMN mine in loam at 4cm depth.

Figure 5: Example of a PMN A-scan in loam.

Figure 6: From left to right, PMN B-scans in sand and in loam and a PMN C-scan in loam.

One advantage of GPR systems is that it is possible to obtain a 3-D representation of objects buried in the ground. In order to recover the correct 3-D shape of buried objects, RMA [15, 18, 19] has developed algorithms based on the convolution, by modelling the behavior of the GPR in the time domain. The developed algorithms are faster than the classical migration methods and provide better results as can be seen on Fig. 7.

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Figure 7: From left to right: raw GPR 3-D image of a PMN mine, restored 3-D image of a PMN mine and restored 3-D image of a 10cm barbed wire with three barbes.

2.2.3 Microwaves radiometers The microwave radiometer is a passive ground penetrating radar constituted by a highly sensitive reception stage which amplifies tremendously the natural radiation captured by its antenna. This radiation is composed by the radiation from the sky (a few K) and reflected by the surface and subsurface and by the natural radiation from the soil [21]. They also can generate clear two-dimensional images of surface, shallowly buried and buried objects (metallic and plastic). The spatial resolution and the penetration depend on the frequency. As for the GPR, the performances are also depending on the soil conditions: a high level of moisture can largely limit the detection capabilities. In the scope of the HOPE project 2, The DLR has developed a MWR which is capable to work at more than 32 different frequencies between 1 and 8 GHz. An example of images obtained with a manual scan is given in Fig. 8.

Figure 8: DLR microwave radiometer images of a metallic plate, obtained with a manual scan, at 8 different frequencies between 1 and 6 GHz.

2.2.4 Other electromagnetic sensors In electrical impedance tomography, the soil impedance is measured between selected locations on the ground. By solving a non-trivial and non-linear inverse problem, it is possible to detect anomalies. In electrography, the corona effect is used to detect explosives (typically TNT) in a liquid phase. The operational characteristics of electromagnetic sensors are described in table 1. 2 The HOPE handheld system, project funded by the European Commission, includes a metal detector (Vallon, GmbH & RMA), a stepped frequency GPR (RST, GmbH) and a multifrequency MWR (DLR), all with imaging capabilities through the use of a high precision positioning system (RMA).

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2.3 Electro-optic sensors Classical cameras have a poor detection capability even for mines laid on the ground and shallow buried objects. LIDAR and therahertz imaging systems have still to demonstrate their usefulness for mine detection. They indeed use shorter wavelengths than ground penetrating radar and hence suffer from significant limitations in soil penetration. Further, wild growing vegetation offers a strong limitation to most electrooptic devices. Nevertheless, special attention must be paid to hyperspectral, thermal infrared sensors and Scanning Laser Doppler Vibrometry (SLDV). 2.3.1 Hyperspectral sensors Hyperspectral techniques take into account the very selective properties of the material reflectivity. Laboratory experiments [3], in the course of which very narrow wavelength bands have been used, have demonstrated the capabilities of wavelength tuning to discriminate between different surface laid materials. 2.3.2 Thermal infrared Mine detection by means of thermal infrared sensors can be achieved in two different approaches. The first approach consists in measuring the apparent temperature difference of the soil, induced by the differences in emissivity and/or by the differences in thermal flux due to the presence of a shallow buried or buried object (see Fig. 9 and [14]). A second approach consists to take advantage of the polarization properties of manufactured surfaces, by analysing the information contained in the images of shallow buried mines produced by the three Stokes parameters which characterize the polarization state, i.e. the degree of polarization, the azimuth and the ellipticity. Those parameters can be evaluated by recording by means of a linear polarizer four different images corresponding to four different polarization directions [9].

Figure 9: From left to right: original image of a buried mine (5 cm below the ground surface), preprocessed image, detected edges and original image with detected mine-like shape (ellipse).

2.3.3 Scanning Laser Doppler Vibrometry (SLDV) In this technology, which is not properly said an electro-optic technology, an acoustic power transmitter sends an acoustic wave in the ground. If an object is present in the soil, at the ground surface a backscattered wave induces soil vibrations measured by a laser Doppler vibrometer. This technique has been tried by FGAN (Germany) on a test site in ISPRA (European Commission Joint Research Centre). Results are shown on Fig. 10. The operational characteristics of electro-optic sensors are described in table 1.

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Figure 10: buried mines acquired with a SLDV (FGAN - Germany).

2.4 Explosive detectors: biosensors 2.4.1 Dogs and educated rodents Actually, one of the most efficient “sensor” for mine detection is the dog. But it appears that rodents are easier to educate and feed and that they can work longer than dogs. Furthermore, rodents are much lighter, have a better olfactory capacity and a better immunity. The non profit organization APOPO and the University of Antwerpen (Belgium) are currently making operational tests in six different countries representative of the mine threat. Rats have already booked interesting results in Tanzania in 2002 [2].

Figure 11: Rodent.

2.4.2 Artificial nose Some research activities have been devoted to technologies that try to mimic the olfactory system of a dog. But up to now no significant results have been booked in the field of humanitarian demining. Another approach consists in using antibodies sensitive to TNT. These antibodies are fixed on a quartz crystal. When the sensor is in contact with TNT free molecules, the fixed antibodies leave the crystal. The result is that the weight of the quartz crystal is changed. This weight loss is measured by measuring the crystal frequency change. A relatively long integration time, due to the extremely low concentration of explosive vapours in the air, makes this technology more suited to confirmation than to the detection itself. Therefore, this technology should be accompanied by a detection equipment such as a metal detector or a GPR or a combination of both. The operational characteristics of biosensors are described in table 1.

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2.5 Explosive detectors: nuclear and chemical methods This family of technologies includes Nuclear Quadripole Resonance, Thermal Neutron Activation (TNA), Fast Neutron Activation (FNA), trace of explosive detection using chemical processes, X-ray backscattering and X-ray fluorescence. Again, the relatively long integration time needed to detect the explosive molecules and their high cost make this technology more suited to confirmation than to the detection itself. Therefore, this technology should be accompanied by a detection equipment such as a metal detector or a GPR or a combination of both. 2.5.1 Nuclear Quadripole Resonance Advanced Nuclear Quadrupole Resonance (NQR) techniques can be used to detect explosives in any surroundings. The quadrupole charge distribution of the atom results in alignments of nuclear spins. A radio frequency pulse (RF-pulse) generated by a transmitter coil causes the excitation of nuclear spins to higher quantized energy levels. When the nuclear spins return to their equilibrium position, they follow a particular precession frequency. This specifies the atoms and functional groups in the molecules. Nitrogen is a quadrupole atom that appears in every type of explosive. Because of very distinct NQR frequencies the false alarm rate due to other nitrogen containing materials is extremely low [5]. Fig. 12 shows respectively a time response of a NQR system and the corresponding spectrum.

Figure 12: From left to right, time domain NQR response and corresponding spectrum.

2.5.2 Thermal Neutron Activation (TNA) Explosive materials are rich in Nitrogen-14 (14 N) which is the predominant stable isotope of Nitrogen. When a neutron is captured by a Nitrogen nucleus the following reactions occur: n

14

N

15

N

15

N γ

The excited Nitrogen nucleus (denoted by ) de-excites to its ground state in less than a picosecond by emitting one or more gamma-rays with unique energies up to 10.83 MeV. The gamma-rays can be detected by conventional detectors such as NaI and/or GeLi. Specific software can be used for signal processing and identification. 2.5.3 Fast Neutron Activation (FNA) Fast (14 MeV) neutrons and the associated alpha particles (3.5 MeV) are generated by a fast neutron source. The neutrons produce prompt gamma rays in inelastic scattering with the nuclei of the materials. Each neutron is always produced in association with an alpha particle which is always emitted at 180 o to the neutron direction; hence, direction of each neutron is known from the direction of each alpha particle associated with it. Alpha particles are detected with an array of scintillation detectors; the position of the detector hit by the alpha determines the alpha direction, which in turn provides the neutron imaging (see Fig. 13). The analysis of the gamma-rays provides information about the stoichiometric composition of 10

hitted materials in terms of Carbon, Nitrogen and Oxigen. Depth information of a given neutron is provided by measuring the time of flight of the corresponding alpha particules.

Figure 13: Image produced by a FNA system, the colours represent the stoichiometric composition in C, N and O (image provided by courtesy of High Energy (CA - USA)).

2.5.4 X-ray backscattering X-ray backscattered radiation is detected during active illumination of the ground with X-rays, and basically determines whether or not an object is predominantly made up of light chemical elements (i.e. low atomic number Z). The technique is intended for bulk explosive detection, although AP mines have been imaged as well; smaller, man-portable detectors based on radioactive sources have also been proposed. The systems which have been developed are said to be able to produce a 2D image with a resolution of a few centimeters. Potential problems come from shallow penetration, system complexity, sensitivity to soil topography and sensor height variation, and safety aspects due to the use of ionizing radiation [6]. 2.5.5 X-ray fluorescence X-rays do not penetrate deeply in the ground. Therefore, they are most of the time not detecting directly the explosive encapsulated in a mine but molecules of explosive which are migrating from the mines to the ground surface. When migrated explosive molecules are illuminated by X-rays, a series of changes occurs in the electron configuration resulting in the emission of photons, characteristic of the material, that can be captured and analysed. 2.5.6 Chemical methods Chemical methods are also used to detect explosive in the air and in the ground. The difficulty consists in collecting enough explosive molecules in the air or in solution because of the very low concentration of explosive available. The operational characteristics are described in table 1.

3 Signal processing and data fusion For each type of sensor, specific signal processing techniques are used in order to extract useful information. The techniques used mainly include signal conditioning or preprocessing (e.g. signal detection, signal transformation, noise reduction, signal restoration and enhancement (see [16], [17] and [23]), which are very important steps before further processing) and pattern recognition techniques aiming at increasing the expertise of each sensor separately. Nevertheless, it has been shown in the previous section that no sensor is perfect for all scenarios and all conditions (moisture, depth, cost, etc).

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Sensor family Prodders & Acoustic

Electromagnetic

Sensor Prodder Smart Prodder Seismic & Acoustic EMI devices Magnetometer Gradiometer GPR MWR Electrical Imp. Tom. Electrography Visible Infrared Infrared Polar.

Electro-optic

Biosensors

Multi & hyperspectal LIDAR Terahertz SLDV Dog Rodents Artificial nose NQR TNA

Nuclear & Chemical

FNA X-ray backscattering X-ray fluorescence Chemical detectors

Maturity In Use In Use In devel. R&D In Use In Use In Use In Use In Devel. R&D R&D OK OK R&D Prototype R&D R&D R&D R&D OK In devel. R&D R&D Prototype R&D Prototype R&D R&D Prototype R&D Prototype R&D

Cost Low Low to Medium

Speed Very Low Very Low

Effectiveness High High

High Low to Medium Low to Medium Low to Medium Medium to High Medium to High Low to Medium Low to Medium Low to Medium High High

Medium Low to Medium Low to Medium Low to Medium Low to Medium Low to Medium Low to Medium Low to Medium Medium Medium Medium

High (in wet soil) High High High High (in dry soil) Medium Unknown Unknown Low Medium Medium

High Very high Very high Very high Medium to high Medium Medium to high Medium to high

Medium Medium Medium Medium Medium to high Medium to high Medium Medium

Medium Low Low Medium to high Medium to high Medium to high Medium Medium

High

Medium

Medium

Very high High

Medium Medium

Very high Low

High

Medium to high

Medium

High

Medium

Unknown

Table 1: Sensor operational characteristics.

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The analysis of the principles of operation of different sensors, their complementary information, and the factors that affect their operability, have led to the conclusion that their fusion should result in improved detectability and reduced number of false alarms in various situations (different types of mines, soil, vegetation, moisture, etc ). Low level fusion can be performed even using a heterogeneous set of sensors if the data are co-registered. Our experience has shown that higher level data fusion is possible but accounting for the following facts:

Learning processes are very difficult and risky because of the inter and intra variability of the scenarios. The heterogeneous character of a given minefield and of the huge set of possible minefields makes generalization unpractical if not dangerous.

High level fusion must rely on qualitative instead of quantitative a priori knowledge, therefore methods like Bayes decision theory will often fail.

The absolute (objective) confidence in specific sensors resulting from extensive trials must be included in the fusion model (principle of objective discounting). The relative (subjective) confidence that the deminer has in specific sensors must also be (interactively) included in the fusion model (principle of subjective discounting).

Since in this domain of application one has to deal with uncertainty, ambiguity, partial knowledge, ignorance and qualitative knowledge, it is important to choose for an approach where they can be appropriately modeled, e.g. belief functions within the framework of the Dempster-Shafer theory. A main motivation for working within this framework is to be able to easily model and include existing knowledge regarding: chosen mine detection sensors, mine laying principles, mines, and objects that can be confused with mines [13]. In any case, we need to be aware that the ultimate decision must belong to the deminer because his life is involved.

4 Conclusions The purpose of this paper is to present the status of sensor technology, including their operational characteristics without having the pretention to be exhaustive. Furthermore, this paper presents the detection as a global process wherein the outputs of the sensors, considered as skilled specialists thanks to their associated processing, can be integrated in a fusion process.

Acknowledgments The authors wish to thank all the researchers of the HUDEM3 and MsMs4 projects without whom it wuold have been impossible to write this paper.

References [1] M. Acheroy. L’infrarouge thermique, principes et applications au déminage humanitaire. Revue HF, (3):p. 13–24, 1998. 3 The Belgian project on humanitarian demining (Hudem) has been funded by the Belgian Ministry of Defence and the Belgian State Secretariat on Development Aid. 4 The Multi-sensor, Multi-signature project is supported by the European Commission Joint Research Centre (ISPRA).

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[2] APOPO - Belgian non profit research organisation that was initiated in response to the global landmine problem - http://www.apopo.org/. [3] F. Dupuis. Acquisition d’images hyperspectrales. Master’s thesis, RMA, Brussels, Belgium, 1998. [4] P. Druyts, L. Merlat and M. Acheroy. Modeling Considerations for Imaging with a Standard Metal Detector. In Detection and Remediation Technologies for Mines and Minelike Targets V, volume 4038, pages 1431–1451, Orlando, FL, USA, April 2000. SPIE Press. [5] A. Engelbeen. Nuclear quadrupole resonance mine detection. In CLAWAR’98, pages pp.249–253, Brussels, Belgium, November 1998. BSMEE. [6] EUDEM2 - Mine Action Technology - http://www.eudem.vub.ac.be. [7] M. T. Hallikainen, F. T. Ulaby, M. C. Dobson, M. A. El-Rayes and L. K. Wu. Microwave dielectric behavior of wet soil—part I: empirical models and experimental observations. IEEE Trans. Geosci. Remote Sensing, GE-23:25–34, 1985. [8] I. Huynen, C. Steukers and F. Duhamel. A wideband line-line dielectrometric method for liquids, soils, and planar substrates. IEEE Trans. Instrumentation and Measurement, 50(5):1343–1348, October 2001. [9] D. L. Jordan and G. D. Lewis. Infrared polarisation signatures. In Proceeding NATO-IRIS Vol. 41 N5, pages p.71–82, BRUSSELS, 1996. NATO-IRIS. [10] S. Lambot, E. C. Slob, I. van den Bosch, B. Stockbroeckx, B. Scheers and M. Vanclooster. GPR design and modeling for identifying the shallow subsurface dielectric properties. In A. Yarovoy, editor, Proceedings of the 2nd International Workshop on Advanced Ground Penetrating Radar, pages 130–135, TU Delft, the Netherlands, May 2003. [11] S. Lambot, E. C. Slob, I. van den Bosch, B. Stockbroeckx, B. Scheers and M. Vanclooster. Estimating soil dielectric properties from monostatic GPR signal inversion in the frequency domain. Water Resources Research, 2003. Accepted with minor revisions. [12] S. Lambot, E. C. Slob, I. van den Bosch, B. Stockbroeckx and M. Vanclooster. Accurate modeling of GPR signal for an accurate characterization of the subsurface dielectric properties. IEEE Transactions on Geoscience and Remote Sensing, 2003. Submitted for publication. [13] N. Milisavljević, I. Bloch and M. Acheroy. A first step towards modeling and combining mine detection sensors within Dempster-Shafer framework. In The 2000 International Conference on Artificial Intelligence (IC-AI’2000), Las Vegas, USA, 2000. [14] N. Milisavljević. Comparison of Three Methods for Shape Recognition in the Case of Mine Detection. Pattern Recognition Letters, 20(11-13):1079–1083, 1999. [15] M. Piette and B. Scheers. Short-pulse response of anti-personnel landmines to UWB GPR signals. In Seventh international conference on ground-penetrating radar, Kansas, USA, 1998. [16] A. Pizurica. Multiresolution techniques for image restoration in mine detection problems. In CLAWAR’98, pages pp.225–230, Brussels, Belgium, November 1998. BSMEE. [17] A. Pizurica, W. Philips, I. Lemahieu and M. Acheroy. Speckle Noise Reduction in GPR Images. In International Symposium on Pattern Recognition “In Memoriam Prof Pierre Devijver”, Brussels, Belgium, Februari 1999. Royal Military Academy, RMA. [18] B. Scheers, M. Piette and A. Vander Vorst. The detection of AP mines using UWB GPR. In IEE Second International Conference, The Detection of Abandoned Land Mines, Edinburgh, UK, October 1998. IEE.

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[19] B. Scheers, M. Piette, M. Acheroy and A. Vander Vorst. A laboratory UWB GPR system for landmine detection. In GPR2000, Sidney, Australia, June 2000. [20] M. Storme, I. Huynen and A. Vander Vorst. Characterization of wet soils in the 2–18 GHz frequency range. Micr. Optical Tech. Lett, 25(5):333–335, June 1999. [21] F. T. Ulaby, R. K. Moore and A. K. Fung. Microwave remote sensing: active and passive. Fundamentals and Radiometry, volume 1. Artech House, 1981. [22] I. van den Bosch, S. Lambot and A. Vander Vorst. A Unified Method for Modeling Radar and Radiometer Measurements. In Proceedings of the EUDEM2 SCOT 2003 conference, volume 2, pages 523–528, VUB, Brussels, 2003. [23] L. van Kempen, H. Sahli, J. Brooks and J. Cornelis. New Results on Clutter Reduction and Parameter Estimation for Landmine Detection Using GPR. In GPR 2000, Eighth International Conference on Ground Penetrating Radar, Gold Coast, Australia, Australia, May 2000. [24] P. Verlinde, M. Acheroy, G. Nesti and A. Sieber. First Results of the Joint Multi-Sensor MineSignatures Measurement Campaign (MsMs Project). In Detection and Remediation Technologies for Mines and Minelike Targets VI, volume 4394, Orlando, FL, USA, April 2001. SPIE Press.

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