Compressed Sensing Based Joint Detection and Tracking for STAP Radar

In this paper, we propose a novel compressed sensing based joint detection and tracking algorithm, named CS-JDT algorithm, to track multiple targets for STAP radar system. A novel general similar sensing matrix pursuit (GSSMP) algorithm is proposed to reconstruct the whole radar scenario (DOA-Doppler plane) for each range gate at consecutive scans. The proposed GSSMP algorithm addresses several problems in existing compressed sensing radar systems: First, it imposes no restrictions on the transmitter since the sensing matrix is built directly on the spatial-temporal steering matrix. There are no constraints on the correlation between any two columns of the sensing matrix since the proposed algorithm can deal with the sensing matrix with high coherence efficiently. Secondly, the size of the compact sensing matrix depends on the threshold of similarity distance used to divide the similar column groups, which does not increase with the resolution of the DOA-Doppler plane. Finally, the GSSMP algorithm can identify the correct subspace quite well, and reconstruct the original K-sparse signal representing the sparse radar scene perfectly, even in the condition of very closely spaced targets.