Micro-Motion Features Analysis for Air Targets Based on Sparse Time-Frequency Decomposition

摘要

Take the helicopter RCS signal as an example, the sparse time-frequency decomposition method is researched in this paper. First, an over-complete dictionary is constructed with Gabor atom. Then the RCS signal is decomposed using matching pursuit algorithm based on Gabor atom dictionary, and the best matching atom is found by genetic algorithm. At last, the signal's WVD distribution with no cross-interference is obtained by superposing the results of each decomposed Gabor atom's WVD transform. The simulation results show that the proposed method can improve the efficiency of sparse decomposition and express the signal WVD with a few Gabor atoms. Compared with Wigner-Ville transform, this method can maintain high time-frequency resolution with no cross-interference terms.