Vol. 48

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2004-06-22

A Multi-Source Strategy Based on a Learning-by-Examples Technique for Buried Object Detection

By Emanuela Bermani, Andrea Boni, Salvatore Caorsi, Massimo Donelli, and Andrea Massa
Progress In Electromagnetics Research, Vol. 48, 185-200, 2004
doi:10.2528/PIER03110701

Abstract

In the framework of buried object detection and subsurface sensing, some of the main difficulties in the reconstruction process are certainly due to the aspect-limited nature of available measurement data and to the requirement of an on-line reconstruction. To limit these problems, a multi-source (MS) learning-by-example (LBE) technique is proposed in this paper. In order to fully exploit the more attractive features of the MS strategy, the proposed approach is based on a support vector machine (SVM). The effectiveness of the MS-LBE technique is evaluated by comparing the achieved results with those obtained by means of a previously developed single-source (SS) SVMbased procedure for an ideal as well as a noisy environment.

Citation

 (See works that cites this article)
Emanuela Bermani, Andrea Boni, Salvatore Caorsi, Massimo Donelli, and Andrea Massa, "A Multi-Source Strategy Based on a Learning-by-Examples Technique for Buried Object Detection," Progress In Electromagnetics Research, Vol. 48, 185-200, 2004.
doi:10.2528/PIER03110701
http://jpier.org/PIER/pier.php?paper=0311071

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