Paper Info
Title
Low-complexity variable forgetting factor mechanisms for adaptive linearly constrained minimum variance beamforming algorithms
Abstract
In this work, the authors propose two low-complexity variable forgetting factor (VFF) mechanisms for recursive least squares-based adaptive beamforming algorithms. The proposed algorithms are designed according to the linearly constrained minimum variance (LCMV) criterion and operate in the generalised sidelobe canceller structure. To obtain a better performance of convergence and tracking, the proposed VFF mechanisms adjust the forgetting factor by employing updated components related to the time-averaged LCMV cost function. They carry out the analyses of the proposed algorithms in terms of the computational complexity and the convergence properties and derive an analytical expression of the steady-state mean-square-error. Simulation results in non-stationary environments are presented, showing that the adaptive beamforming algorithms with the proposed VFF mechanisms outperform the existing methods at a significantly reduced complexity.
Year
DOI
Venue
2015
10.1049/iet-spr.2014.0013
IET Signal Processing
Keywords
Field
DocType
array signal processing,computational complexity,convergence,regression analysis,vff mechanism,adaptive linearly constrained minimum variance beamforming algorithms,convergence properties,generalised sidelobe canceller structure,low-complexity variable forgetting factor mechanisms,recursive least squares-based adaptive beamforming algorithms,time-averaged lcmv cost function
Convergence (routing),Minimum-variance unbiased estimator,Mathematical optimization,Forgetting factor,Adaptive beamformer,Algorithm,Mathematics,Minimum variance beamforming,Recursive least squares filter,Computational complexity theory
Journal
Volume
Issue
ISSN
9
2
1751-9675
Citations 
PageRank 
References 
5
0.41
14
Authors
3
Name
Order
Citations
PageRank
Linzheng Qiu1151.17
Yunlong Cai223825.39
Minjian Zhao311827.18