Robust Low-Rank LCMV Beamforming Algorithms Based on Joint Iterative Optimization Strategies
R. C. de Lamare

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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsDirection-of-Arrival Estimation Techniques · Advanced Adaptive Filtering Techniques · Speech and Audio Processing
