A low-complexity variable forgetting factor constant modulus RLS algorithm for blind adaptive beamforming

In this paper, a recursive least squares (RLS) based blind adaptive beamforming algorithm that features a new variable forgetting factor (VFF) mechanism is presented. The beamformer is designed according to the constrained constant modulus (CCM) criterion, and the proposed adaptive algorithm operate...

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Veröffentlicht in:Signal processing Jg. 105; S. 277 - 282
Hauptverfasser: Qin, Boya, Cai, Yunlong, Champagne, Benoit, de Lamare, Rodrigo C., Zhao, Minjian
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier B.V 01.12.2014
Elsevier
Schlagworte:
Adaptive beamforming
Applied sciences
Constrained constant modulus
Detection, estimation, filtering, equalization, prediction
Exact sciences and technology
Information, signal and communications theory
Recursive least square
Signal and communications theory
Signal, noise
Telecommunications and information theory
Variable forgetting factor
Recursive least square
Variable forgetting factor
Adaptive beamforming
Constrained constant modulus
Performance evaluation
Target tracking
Adaptive algorithm
Forgetting factor
Noise reduction
Recursive algorithm
Steady state
Beam forming
Mean square error
Constant modulus algorithm
Learning
Interference suppression
Current mode
Least squares method
Recursive method
Signal processing
Convexity
ISSN:0165-1684, 1872-7557
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:In this paper, a recursive least squares (RLS) based blind adaptive beamforming algorithm that features a new variable forgetting factor (VFF) mechanism is presented. The beamformer is designed according to the constrained constant modulus (CCM) criterion, and the proposed adaptive algorithm operates in the generalized sidelobe canceler (GSC) structure. A detailed study of its operating properties is carried out, including a convexity analysis and a mean squared error (MSE) analysis of its steady-state behavior. The results of numerical experiments demonstrate that the proposed VFF mechanism achieves a superior learning and tracking performance compared to other VFF mechanisms. •A low-complexity variable forgetting factor mechanism is proposed for adaptive CCM beamforming.•The convexity analysis and the steady-state mean-square error analysis are carried out.•The numerical results demonstrate that the proposed algorithm achieves a superior performance.
ISSN:0165-1684
1872-7557
DOI:10.1016/j.sigpro.2014.06.003