An iterative algorithm for the computation of the MVDR filter
IEEE Transactions on Signal Processing
Spatial diversity in radars-models and detection performance
IEEE Transactions on Signal Processing
MIMO Radar Space–Time Adaptive Processing Using Prolate Spheroidal Wave Functions
IEEE Transactions on Signal Processing
A multistage representation of the Wiener filter based on orthogonal projections
IEEE Transactions on Information Theory
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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 reducedrank algorithms. Simulations show that the proposed algorithm outperforms existing reduced-rank algorithms.