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

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.