Reduced-rank STAP for MIMO radar based on joint iterative optimization of knowledge-aided adaptive filters

摘要

MIMO radar has received significant attention in the past five years. In this paper, we focus on the advantage of MIMO radars in achieving better spatial resolution by employing the colocated antennas and propose a reduced-rank knowledge-aided technique for MIMO radar space-time adaptive processing (STAP) design. The scheme is based on joint iterative optimization of knowledge-aided adaptive filters (JIOKAF) and takes advantage of the prior environmental knowledge by employing linear constraint techniques. 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 PD performance, in comparison with the state-of-the-art reduced-rank algorithms. Simulations show that the proposed algorithm outperforms existing reduced-rank algorithms.