Sparse Representation for Target Parameter Estimation in CDR-Based Passive Radar

摘要

In the China digital radio (CDR)-based passive radar, when the range-Doppler (RD) map is generated by the classical matched filtering method, the target range profile has the mainlobe splitting phenomenon. To solve this problem, we first derive the reason for the target mainlobe splitting from the subcarrier domain. Then, we develop an effective-subcarrier-based complex fast sparse Bayesian learning (ES-CFSBL) algorithm to generate RD map. Finally, simulation and experimental results show that the developed algorithm can effectively reduce sidelobe level and make it easier to detect and track weak targets covered by strong target sidelobes. Besides, the proposed method can realize fractional delay estimation conveniently.