A neural network for robust LCMP beamforming

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

17 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)2901-2912
Journal / PublicationSignal Processing
Volume86
Issue number10
Publication statusPublished - Oct 2006

Abstract

Calculating an optimal beamforming weight is a main task of beamforming. Quadratic constraints on the weight vector of an adaptive linearly constrained minimum power (LCMP) beamformer can improve robustness to pointing errors and to random perturbations in sensor parameters. This paper presents a neural network approach to the robust LCMP beamformer with the quadratic constraint. Compared with the existing neural networks for the LCMP beamformer, the proposed neural network converges fast to an optimal weight. Compared with the existing adaptive algorithms for the robust LCMP beamformer, in addition to parallel implementation, the proposed neural network is guaranteed to converge exponentially to an optimal weight. Simulations demonstrate that the proposed neural network has better interference suppression and faster convergence than the existing neural networks and the adaptive algorithms. © 2006 Elsevier B.V. All rights reserved.

Research Area(s)

  • Convergence analysis, LCMP beamforming, Quadratic constraint, Recurrent neural network

Citation Format(s)

A neural network for robust LCMP beamforming. / Xia, Youshen; Feng, Gang.
In: Signal Processing, Vol. 86, No. 10, 10.2006, p. 2901-2912.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review