Distributed sparse MVDR beamforming using the bi-alternating direction method of multipliers

Abstract

Until now, distributed acoustic beamforming has focused on optimizing for a beamformer over an entire network, with each node contributing to the beamformer output. We present a novel approach that introduces sparsity to this beamformer computation, where we attempt to optimize for a subset of nodes within the network that produce SNR gains roughly equivalent to that of the optimal MVDR case. Due to the physical nature of sound, this approach trades a small loss in SNR for a large reduction in communication power and iterations required to produce a beamformer output by reducing the active node set of our network. Our approach operates in a fully distributed and asynchronous manner and does not require a high update iteration rate to produce an output at each sample.