A distributed algorithm for robust LCMV beamforming
Thomas Sherson (TU Delft - Signal Processing Systems)
W.B. Kleijn (TU Delft - Signal Processing Systems)
Richard Heusdens (TU Delft - Signal Processing Systems)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
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.