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

TL;DR

This paper introduces a low-complexity adaptive reduced-rank LCMV beamforming algorithm using set-membership filtering and joint iterative optimization, improving performance and robustness in array signal processing.

Contribution

It presents a novel stochastic gradient-based algorithm with a dynamic bound for adaptive reduced-rank beamforming, enhancing convergence and stability.

Findings

Outperforms existing full-rank and reduced-rank methods in simulations

Reduces computational complexity while maintaining accuracy

Effectively adjusts step sizes to prevent misadjustment

Abstract

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