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International Journal of Antennas and Propagation
Volume 2014 (2014), Article ID 930581, 9 pages
http://dx.doi.org/10.1155/2014/930581
Research Article

Comparison of Matrix Pencil Extracted Features in Time Domain and in Frequency Domain for Radar Target Classification

1Clermont Université, Université Blaise Pascal, BP 10448, 63000 Clermont Ferrand, France
2CNRS, UMR 6602, Institut Pascal, 63177 Aubière, France
3ISRI, 300 Cheoncheon-Dong, Jangan-Gu, Suwon, Gyeonggi-Do 440-746, Republic of Korea

Received 5 November 2013; Accepted 24 December 2013; Published 4 February 2014

Academic Editor: Tayeb A. Denidni

Copyright © 2014 Mahmoud Khodjet-Kesba et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Feature extraction is a challenging problem in radar target identification. In this paper, we propose a new approach of feature extraction by using Matrix Pencil Method in Frequency Domain (MPMFD). The proposed method takes into account not only the magnitude of the signal, but also its phase, so that all the physical characteristics of the target will be considered. With this method, the separation between the early time and the late time is not necessary. The proposed method is compared to Matrix Pencil Method in Time Domain (MPMTD). The methods are applied on UWB backscattered signal from three canonical targets (thin wire, sphere, and cylinder). MPMFD is applied on a complex field (real and imaginary parts of the signal). To the best of our knowledge, this comparison and the reconstruction of the complex electromagnetic field by MPMFD have not been done before. We show the effect of the two extraction methods on the accuracy of three different classifiers: Naïve bayes (NB), K-Nearest Neighbor (K-NN), and Support Vector Machine (SVM). The results show that the accuracy of classification is better when using extracted features by MPMFD with SVM.