Adaptive reduced-rank LCMV beamforming algorithms based on joint iterative optimization of filters: Design and analysis

作者：de Lamare R C^*; Wang L; Fa R

来源：Signal Processing, 2010, 90(2): 640-652.

DOI：10.1016/j.sigpro.2009.08.002

• Adaptive filters
• Beamforming
• Constrained optimization
• Iterative methods

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.

• 出版日期2010-2