LFM signal detection and estimation based on sparse representation

This paper presents a novel approach for detection and estimation of fundamental parameters of linear frequency modulation (LFM) signals, i.e., the initial frequency and Chirp rate. The proposed approach is based on sparse representation of noisy input signals over two specific dictionaries, each designed for finding a parameter of LFM signal. Moreover, an iterative framework is proposed for simultaneous sparse representation over the two dictionaries. Experimental results demonstrate that the presented method is comparable with the optimum transform for LFM signal estimation, Wigner-Hough, and furthermore, has significantly higher speed.