Parameter Estimation of Frequency-hopping Signals Based on Sparse Time-frequency Distribution

In the case of parameter estimation of Frequency Hopping (FH) signal based on conventional time-frequency analysis, the suppression of cross-terms in Time-Frequency Distribution (TFD) by kernel function always leads to the decrease of time-frequency concentration, which is adverse to signal parameter extraction. To deal with this problem, a kind of Sparse TFD (STFD) based FH signals processing method is proposed. Based on the principle of Cohen's class of TFD and the ambiguity function characteristics of FH signals, a Rectangle-shaped Kernel Distribution (RKD) is constructed by choosing the rectangle function in ambiguity domain as its kernel function. RKD can suppress the cross-terms effectively but is followed by poor time-frequency resolution. In order to improve the performance of RKD, the TFD sparsity of FH signals is analyzed and utilized, and the optimal model of STFD is established by additional constraints to RKD under the Compressed Sensing (CS) frame. STFD can not only restrain cross-terms effectively, but also has a high time-frequency concentration. Simulation results show that proposed STFD based parameter estimation of FH signals has better performance compared with conventional ones.