Fast Algorithm on Parameter Estimation of Wideband LFM Signal Based on Down-chirp and CS

Compressed Sensing (CS) has been successfully applied to the parameter estimation of Linear Frequency Modulation (LFM) signal. Compared to the Nyquist sampling method, far less samples are needed to estimate the frequency parameter. However, the super-resolution estimation of frequency parameter can greatly increase the number of atoms in the over-complete dictionary and it will brings a huge amount of computation. This paper proposes resolutions to this problem. Taking the feature of CS into account that the sampling and compression are completed at the same time, we structure a measurement matrix which can complete the compressive sampling and down-chirp simultaneously. Furthermore, we propose a down-chirp based method and improve it with fast computing strategy to solve the above problem. Simulation results have proved that the frequency parameter can be accurate estimated under a low SNR and sampling condition. Meanwhile, compared with the proposed method, the improved algorithm greatly reduces the scale of over-complete dictionary and the amount of computation, and the estimation time has been cut down significantly.