System identification based on the sparse representation of signals
基于稀疏表示的系统辨识方法

On the basis of sparse representation of signals, a novel method is proposed to identify the linear timeinvariant system in low signal-to-noise ratio environment. This method employs the chirp signals as the input to the identified system, and let the output be processed before identification by using the matching pursuit algorithm for noisereduction. Because of the time-frequency localization property of the input and output signals, a large amount of additive white noise can be reduced and the performance of system identification is thus improved. Simulation results show that the proposed method outperforms the conventional methods significantly in very low signal-to-noise ratio environment.