Low-Complexity Constrained Adaptive Reduced-Rank Beamforming Algorithms

Author

Lei Wang ; DeLamare, Rodrigo C.

Author_Institution

Dept. of Electron., Univ. of York, York, UK

Volume

49

Issue

4

fYear

2013

fDate

Oct-13

Firstpage

2114

Lastpage

2128

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

Keywords

adaptive filters; array signal processing; gradient methods; least squares approximations; radar signal processing; recursive estimation; stochastic processes; RLS; SG; SMF; computational cost; enhanced convergence performance; low-complexity constrained adaptive reduced-rank beamforming algorithm; radar system; recursive least square adaptive algorithm; set-membership filtering technique; stochastic gradient; tracking performance; Adaptive algorithms; Array signal processing; Arrays; Convergence; Optimization; Signal processing algorithms; Vectors;

fLanguage

English

Journal_Title

Aerospace and Electronic Systems, IEEE Transactions on

Publisher

ieee

ISSN

0018-9251

Type

jour

DOI

10.1109/TAES.2013.6621805

Filename

6621805