A novel feature extraction approach for radar emitter signals

Feature extraction is the crucial technology to deinterleave and recognize the new system radar emitter signals. In this paper, a novel time-frequency atom feature extraction approach is presented. Based on the over-complete multiscale dictionary of Gaussian Chirplet atoms, adopting Match Pursuit (MP) to decompose signals and the improved quantum genetic algorithm (IQGA) to reduce the search time for MP, the optimal Chirplet atoms to represent the feature information of the radar emitter signals can be obtained. The validity and feasibility of the approach was proved by using fewer Chirplet atoms to acquire more accurate feature information compared with Gabor atoms approach.