Off-Grid Chirp-Rate Estimation for LFM Signals Based on Dynamic Dictionary

Under the compressive sensing (CS) framework, a chirp-rate estimation method for multi-component linear frequency modulation (LFM) signals in the off-grid case is proposed, where the chirp-rates and the corresponding off-grid bias terms are estimated separately for complexity reduction. Then, an iterative off-grid chirp-rate estimation method based on dynamic dictionary is presented, where the estimated chirp-rates are iteratively employed to generate the extremely sparse dictionary. Improved estimation accuracy is achieved by alleviating the offgrid approximation error gradually, and the complexity caused by the iterations is limited due to the small number of parameters to be optimized (equal to the number of signals).