KNOWLEDGE-AIDED REDUCED-RANK STAP FOR MIMO RADAR BASED ON JOINT ITERATIVE CONSTRAINED OPTIMIZATION OF ADAPTIVE FILTERS WITH MULTIPLE CONSTRAINTS

In this paper, a reduced-rank knowledge-aided technique for MIMO radar space-time adaptive processing (STAP) design is proposed. We focus on the advantage of MIMO radars in achieving better spatial resolution by employing the colocated antennas. The scheme is based on knowledge-aided constrained joint iterative optimization of adaptive filters (KAC-JIOAF) and takes advantage of the a priori covariance matrix by employing additional linear constraints in the design. A recursive least squares (RLS) implementation is derived to reduce the computational complexity. We evaluate the algorithm in terms of signal-to-interference-plus-noise ratio (SINR) and probability of detection P-D performance and compare it with the state-of-the-art reduced-rank algorithms. Simulations show that the proposed algorithm outperforms existing reduced-rank algorithms.