FEATURE EXTRACTION OF RADAR EMITTER SIGNALS BASED ON GAUSSIAN CHIRPLET ATOMS
基于Chirplet原子的雷达辐射源信号特征提取

Feature extraction is a crucial technology in advanced radar emitter signal deinterleaving and recognition.A time-frequency atom approach to extract the features of radar emitter signals was presented in this study.Based on the over-complete multiscale dictionary of Gaussian Chirplet atoms,the signals were decomposed into a linear expansion of atoms by the method of marching pursuit(MP).Then,the improved quantum genetic algorithm was applied to effectively reduce the time-complexity at each search step of MP,and thus some intrinsic Chirplet atoms describing features of signals were obtained.Experiment results show that the Chirplet atom is better than the Gabor atom in extracting the feature parameters,which confirms the validity and feasibility of the approach.