A distributed algorithm for robust LCMV beamforming

In this paper we propose a distributed reformulation of the linearly constrained minimum variance (LCMV) beamformer for use in acoustic wireless sensor networks. The proposed distributed minimum variance (DMV) algorithm, for which we demonstrate implementations for both cyclic and acyclic networks,...

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Bibliographic Details
Published in:Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) pp. 101 - 105
Main Authors: Sherson, Thomas, Kleijn, W. Bastiaan, Heusdens, Richard
Format: Conference Proceeding Journal Article
Language:English
Published: IEEE 01.03.2016
Subjects:
acoustic
Acoustics
Algorithms
Array signal processing
Beamforming
Computation
Covariance matrices
Distributed
Electronics
LCMV
Microphones
Networks
Noise
Optimization
Robustness
Variance
Wireless sensor networks
ISSN:2379-190X
Online Access:Get full text
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Summary:In this paper we propose a distributed reformulation of the linearly constrained minimum variance (LCMV) beamformer for use in acoustic wireless sensor networks. The proposed distributed minimum variance (DMV) algorithm, for which we demonstrate implementations for both cyclic and acyclic networks, allows the optimal beamformer output to be computed at each node without the need for sharing raw data within the network. By exploiting the low rank structure of estimated covariance matrices in time-varying noise fields, the algorithm can also provide a reduction in the total amount of data transmitted during computation when compared to centralised solutions. This is particularly true when multiple microphones are used per node. We also compare the performance of DMV with state of the art distributed beamformers and demonstrate that it achieves greater improvements in SNR in dynamic noise fields with similar transmission costs.
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ISSN:2379-190X
DOI:10.1109/ICASSP.2016.7471645