Robust Low-Rank LCMV Beamforming Algorithms Based on Joint Iterative Optimization Strategies

TL;DR

This paper introduces robust low-rank LCMV beamforming algorithms utilizing joint iterative optimization, improving convergence and tracking in uncertain environments with comparable complexity to existing methods.

Contribution

It proposes a novel reduced-rank LCMV scheme based on joint iterative optimization of parameters, including rank-reduction, beamforming, and diagonal loading, with efficient adaptive algorithms.

Findings

Outperforms existing algorithms in convergence and tracking

Effective in uncertain environments

Maintains comparable computational complexity

Abstract

This chapter presents reduced-rank linearly constrained minimum variance (LCMV) algorithms based on the concept of joint iterative optimization of parameters. The proposed reduced-rank scheme is based on a constrained robust joint iterative optimization (RJIO) of parameters according to the minimum variance criterion. The robust optimization procedure adjusts the parameters of a rank-reduction matrix, a reduced-rank beamformer and the diagonal loading in an alternating manner. LCMV expressions are developed for the design of the rank-reduction matrix and the reduced-rank beamformer. Stochastic gradient and recursive least-squares adaptive algorithms are then devised for an efficient implementation of the RJIO robust beamforming technique. Simulations for a application in the presence of uncertainties show that the RJIO scheme and algorithms outperform in convergence and tracking performances existing algorithms while requiring a comparable complexity.