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In this paper, a system for automatically recognizing radar waveforms is introduced. This type of techniques are needed in various spectrum management, surveillance and cognitive radio or radar applications. The intercepted radar signal is classified to eight classes based on the pulse compression waveform: linear frequency modulation (LFM), discrete frequency codes (Costas codes), binary phase, and Frank, P1, P2, P3, and P4 polyphase codes. The classification system is a supervised classification system that is based on features extracted from the intercepted radar signal. A large set of potential features are presented. New features based on Wigner and Choi-Williams time-frequency distributions are proposed. The feature set is pruned by discarding redundant features using an information theoretic feature selection algorithm. The performance of the classification system is analyzed using extensive simulations. Simulation results show that the classification system achieves overall correct classification rate of 98% at signal-to-noise ratio (SNR) of 6 dB on data similar to the training data
Lundn et al. (Thu,) studied this question.