The assessment of the performances of ground-penetrating radar (GPR)
in humanitarian demining is an important problem. These performances
are related to the relative strength of the target radar response with respect to that of the soil. Many parameters influence both responses. The physical and geometrical parameters that influence the target signature include the soil electromagnetic (EM) constitutive parameters, the target depth and orientation with respect to the soil surface, the antenna height and the target EM and geometrical properties.
This work presents a numerical parametric study of the soil and target radar signatures. The advantages of the numerical approach are: it allows for a separate study of the influence of each parameters on the radar responses, it is fast, cheap, generic with regards to hardware, and finally it is not prone to experimental errors and hardware failures or misuse. Moreover it is always possible to link the numerical experiments with a particular hardware by characterizing this latter. However, a number of simplifications, such as modeling the soil as a planar multilayered medium, are introduced to keep the problem tractable.
This study yields surprising results, such as for example the possibility of considering the target in homogeneous space for computing its signature, as soon as it is a few centimeters deep. The target considered in the numerical experiments is a dielectric cylinder representing an AP mine, with diameter 6 cm and height 5 cm, and εrt=3. These values are chosen to approach as much as possible the physical properties of the M35BG AP mine, which is small and therefore difficult to detect.
This paper analyzes the effect of the soil on the response of a metal detector (MD). The total response is first decomposed in a direct coupling between the transmitter and the receiver, the mine contribution and the soil contribution. The mine contribution is further related to its free space signature by introducting a number of transfer functions (TFs). Those TFs characterize the effect of the soil on the field propagation, from the transmit coil to the mine and back to the receiver, and on the mine signature. The expressions derived are quite general. However the TFs and other quantities of interest can only be computed if the scattering problem has been solved. For this it is usually necessary to resort to numerical techniques. Such techniques are computationally expensive, especially to analyze the various effects of the soil as they require to compute the solution for a large set of parameters. Therefore, we propose to model a buried mine by a multilayered sphere. From outside to inside, the layers represent the air, the soil, the mine explosive and the mine metallic content. Further, the analytic solution for such a multilayered sphere is used to compute the mine and soil responses, the mine free space signature and the various TFs as a function of the parameters of interest such as the soil electromagnetic (EM) properties or the mine depth. Finally, the validity domain of a number of practical approximations is discussed.
A standard pulsed induction metal. detector is used to image buried metallic objects by scanning an area of interest. It is shown that, under specific hypotheses, the output image is the result of the convolution of a target function with a kernel depending on the incident magnetic field. Several hypotheses are considered, leading to different kernel shapes and different interpretations of the target function. As the detector imaging function is a low-pass filter, shape's details spread out and the resulting raw image are blurred, Since a high-pass restoration filter must be used to deconvolve the raw images, care must be taken to avoid a strong amplification of noise. The imaging filter is computed using a numerical simulation of the incident magnetic field. Finally, the restoration filter is computed using the Wiener approach. Results are shown for a couple of metallic pieces.
The aim of this paper is to propose a strategy that uses data fusion at three different levels to gradually improve the performance of an identity verification system. In a first step temporal data fusion can be used to combine multiple instances of a single (mono-modal) expert to reduce its measurement variance. If system performance after this first step is not good enough to satisfy the end-user's needs, one can improve it by fusing in a second step result of multiple experts working on the same modality. For this approach to work, it is supposed that the respective classification errors of the different experts are de-correlated. Finally, if the verification system's performance after this second step is still not good enough, one will be forced to move onto the third step in which performance can be improved by using multiple experts working on different (biometric) modalities. To be useful however, these experts have to be chosen in such a way that adding the extra modalities increases the separation in the multi-dimensional modality-space between the distributions of the different populations that have to be classified by the system. This kind of level-based strategy allow to gradually tune the performance of an identity verification system to the end-user's requirements while controlling the increase of investment costs. In this paper results of several fusion modules will be shown at each level. All experiments have been performed on the same multi-modal database to be able to compare the gain in performance each time one goes up a level.
Hispars is an European EUCLID investigation projected devoted to evaluation of Artificial Neural Networks for defense pattern applications. Three demonstrators representing three military operational contexts (Air-to- Ground, Ground Battlefield, Naval Threat Evaluation) have been defined and developed. A set of operational processing chains have been selected, and for each of them, ANN methods have been proposed and evaluated on real data set at each level of processing, in comparison to those classical techniques used in existing equipment.
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