An innovative real-time technique for buried object detection

A new online inverse scattering methodology is proposed. The original problem is recast into a regression estimation and successively solved by means of a support vector machine (SVM). Although the approach can be applied to various inverse scattering applications, it is very suitable for dealing with buried object detection. The application of SVMs to the solution of such problems is firstly illustrated. Then some examples, concerning the localization of a given object from scattered field data acquired at a number of measurement points, are presented. The effectiveness of the SVM method is evaluated in comparison with classical neural network based approaches.

[1]  Davide Anguita,et al.  Evaluating the Generalization Ability of Support Vector Machines through the Bootstrap , 2000, Neural Processing Letters.

[2]  Salvatore Caorsi,et al.  Perfectly matched layers for the truncation of finite‐element meshes in layered half‐space geometries and applications to electromagnetic scattering by buried objects , 1998 .

[3]  Krzysztof A. Michalski,et al.  A neural-network approach to the electromagnetic imaging of elliptic conducting cylinders , 2001 .

[4]  R. Kleinman,et al.  Microwave imaging-Location and shape reconstruction from multifrequency scattering data , 1997 .

[5]  Michael Georgiopoulos,et al.  Applications of Neural Networks in Electromagnetics , 2001 .

[6]  Dimitri P. Bertsekas,et al.  Nonlinear Programming , 1997 .

[7]  Matteo Pastorino,et al.  Numerical electromagnetic inverse-scattering solutions for two-dimensional infinite dielectric cylinders buried in a lossy half-space , 1993 .

[8]  Takashi Takenaka,et al.  Conjugate gradient method applied to inverse scattering problem , 1995 .

[9]  Matteo Pastorino,et al.  A computational technique based on a real-coded genetic algorithm for microwave imaging purposes , 2000, IEEE Trans. Geosci. Remote. Sens..

[10]  Ioannis T. Rekanos,et al.  Inverse scattering of dielectric cylinders by using radial basis function neural networks , 2001 .

[11]  Siyuan Chen,et al.  Inverse scattering of two-dimensional dielectric objects buried in a lossy earth using the distorted Born iterative method , 2001, IEEE Trans. Geosci. Remote. Sens..

[12]  Paolo Gamba,et al.  Electromagnetic detection of dielectric cylinders by a neural network approach , 1999, IEEE Trans. Geosci. Remote. Sens..

[13]  Dustin Boswell,et al.  Introduction to Support Vector Machines , 2002 .

[14]  Levent Gürel,et al.  Frequency responses of ground-penetrating radars operating over highly lossy grounds , 2002, IEEE Trans. Geosci. Remote. Sens..

[15]  Peter M. van den Berg,et al.  Characterization of a two-dimensional subsurface object with an effective scattering model , 1999, IEEE Trans. Geosci. Remote. Sens..

[16]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[17]  Salvatore Caorsi,et al.  An Inverse Scattering Approach Based On a Neural Network Technique for the Detection of Dielectric Cylinders Buried in a Lossy Half-Space - Abstract , 2000 .

[18]  Andrea Massa,et al.  On the training patterns of a neural network for target localization in the spatial domain , 2001 .

[19]  Salvatore Caorsi,et al.  An Inverse Scattering Approach Based on a Neural Network Technique for the Detection of Dielectric Cylinders Buried in a Lossy Half-Space , 2000 .

[20]  Ioannis T. Rekanos.  On-line inverse scattering of conducting cylinders using radial basis-function neural networks , 2001 .

[21]  John C. Platt,et al.  Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .