Classification of Radar Targets using Features Based on Warped Discrete Fourier Transform

In this research the signals from ground surveillance radar audio output are classified using hierarchical nonlinear classifier based on Support Vector Machine. Central Doppler frequency and spectral width around it are used as features. These features are obtained based on spectrogram calculated using discrete Fourier transform and spectrogram calculated using warped discrete Fourier transform. The performance of used features for classification are analyzed through classification accuracy. Obtained results show that features extracted from the spectrogram calculated using warped discrete Fourier transform provide higher classification accuracy.