Distributed Node-Specific LCMV Beamforming in Wireless Sensor Networks

Alexander Bertrand, Marc Moonen
2012 IEEE Transactions on Signal Processing  
In this paper, we consider the linearly constrained distributed adaptive node-specific signal estimation (LC-DANSE) algorithm, which generates a node-specific linearly constrained minimum variance (LCMV) beamformer, i.e., with node-specific linear constraints, at each node of a wireless sensor network. The algorithm significantly reduces the number of signals that are exchanged between nodes, and yet obtains the optimal LCMV beamformers as if each node has access to all the signals in the
more » ... ignals in the network. We consider the case where all the steering vectors are known, as well as the blind beamforming case where the steering vectors are not known. We formally prove convergence and optimality for both versions of the LC-DANSE algorithm. We also consider the case where nodes update their local beamformers simultaneously instead of sequentially, and we demonstrate by means of simulations that applying a relaxation is often required to obtain a converging algorithm in this case. We also provide simulation results that demonstrate the effectiveness of the algorithm in a realistic speech enhancement scenario. EDICS: SAM-BEAM Beamforming, SAM-MCHA Multichannel processing, SEN-FUSE Data fusion from multiple sensors Index Terms-Wireless sensor networks, wireless acoustic sensor networks, distributed estimation, beamforming, LCMV beamforming
doi:10.1109/tsp.2011.2169409 fatcat:vmkdjdszyfhtrbrks3huxboo7u