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.
Low-Complexity Constrained Adaptive Reduced-Rank Beamforming Algorithms
IEEE Transactions on Aerospace and Electronic Systems ; 49 , 4 ; 2114-2128
2013-10-01
1833354 byte
Article (Journal)
Electronic Resource
English
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