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This paper proposes a new adaptive algorithm for the implementation of thelinearly constrained minimum variance (LCMV) beamformer. The proposed algorithmutilizes the set-membership filtering (SMF) framework and the reduced-rankjoint iterative optimization (JIO) scheme. We develop a stochastic gradient(SG) based algorithm for the beamformer design. An effective time-varying boundis employed in the proposed method to adjust the step sizes, avoid themisadjustment and the risk of overbounding or underbounding. Simulations areperformed to show the improved performance of the proposed algorithm incomparison with existing full-rank and reduced-rank methods.

Low-Complexity Adaptive Set-Membership Reduced-rank LCMV Beamforming