Target Classification with FMCW Radar Using Features Extracted from Fourier Transform of Radar Cross Section

In this paper, classification of target based on Artificial Neural Networks using 30 GHz Monostatic Narrowband FMCW Radar is studied. During classification, features extracted from Radar Cross Section (RCS) information of targets are used. This study is differentiates from the studies in the literature by the ways of using features extracted from Fourier Transform of RCS information. The performance of classifier is tested with realistically prepared synthetic data. Success rate of classifier is found that %91.8.