Low-Complexity Constrained Adaptive Reduced-Rank Beamforming Algorithms

摘要

A reduced-rank framework with set-membership filtering (SMF) techniques is presented for adaptive beamforming problems encountered in radar systems. We develop and analyze stochastic gradient (SG) and recursive least squares (RLS)-type adaptive algorithms, which achieve an enhanced convergence and tracking performance with low computational cost, as compared with existing techniques. Simulations show that the proposed algorithms have a superior performance to prior methods, while the complexity is lower.