Adaptive filter theory (2nd ed.)
Adaptive filter theory (2nd ed.)
Reduced-rank adaptive filtering using Krylov subspace
EURASIP Journal on Applied Signal Processing
A fast least-squares algorithm for linearly constrained adaptivefiltering
IEEE Transactions on Signal Processing
An eigenanalysis interference canceler
IEEE Transactions on Signal Processing
A projection approach for robust adaptive beamforming
IEEE Transactions on Signal Processing
Adaptive convergence of linearly constrained beamformers based onthe sample covariance matrix
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing - Part I
Optimal reduced-rank estimation and filtering
IEEE Transactions on Signal Processing
A multistage representation of the Wiener filter based on orthogonal projections
IEEE Transactions on Information Theory
IEEE Transactions on Signal Processing
A class of constrained adaptive beamforming algorithms based on uniform linear arrays
IEEE Transactions on Signal Processing
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This paper presents reduced-rank linearly constrained minimum variance (LCMV) beamforming algorithms based on joint iterative optimization of filters. The proposed reduced-rank scheme is based on a constrained joint iterative optimization of filters according to the minimum variance criterion. The proposed optimization procedure adjusts the parameters of a projection matrix and an adaptive reduced-rank filter that operates at the output of the bank of filters. We describe LCMV expressions for the design of the projection matrix and the reduced-rank filter. We then describe stochastic gradient and develop recursive least-squares adaptive algorithms for their efficient implementation along with automatic rank selection techniques. An analysis of the stability and the convergence properties of the proposed algorithms is presented and semi-analytical expressions are derived for predicting their mean squared error (MSE) performance. Simulations for a beamforming application show that the proposed scheme and algorithms outperform in convergence and tracking the existing full-rank and reduced-rank algorithms while requiring comparable complexity.