A distributed algorithm for robust LCMV beamforming

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16 Citations (Scopus)
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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.
Original languageEnglish
Title of host publication2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Subtitle of host publicationProceedings
EditorsMin Dong, Thomas Fang Zheng
Place of PublicationDanvers, MA
Number of pages5
ISBN (Electronic)978-1-4799-9988-0
Publication statusPublished - 19 May 2016
Event2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Shanghai International Convention Center, Shanghai, China
Duration: 20 Mar 201625 Mar 2016


Conference2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016
Abbreviated titleICASSP

Bibliographical note

Accepted Author Manuscript


  • beamforming
  • Distributed
  • LCMV
  • acoustic


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