Low-Complexity Reduced-Rank Beamforming Algorithms

TL;DR

This paper introduces low-complexity reduced-rank adaptive beamforming algorithms using set-membership filtering, demonstrating improved convergence, tracking, and computational efficiency in radar systems.

Contribution

It develops and analyzes stochastic gradient and RLS-type algorithms within a reduced-rank set-membership framework for adaptive beamforming.

Findings

Algorithms outperform prior methods in simulations.

Enhanced convergence and tracking performance.

Lower computational complexity achieved.

Abstract

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 to existing techniques. Simulations show that the proposed algorithms have a superior performance to prior methods, while the complexity is lower.