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Low-Complexity Reduced-Rank Beamforming Algorithms

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 to existing techniques. Simulations show that the proposed algorithms have a superior performance to prior methods, while the complexity is lower.

preprint2013arXivOpen access
L. WangR. C. de Lamare
math.ITInformation Theory
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L. WangR. C. de Lamare

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