Convex Optimization for Distributed Acoustic Beamforming

<p>Beamforming filter optimization can be performed over a distributed wireless sensor network, but the output calculation remains either centralized or linked in time to the weights optimization. We propose a distributed method for calculating the beamformer output which is independent of the filter optimization. The new method trades a small decrease in signal to noise performance for a large decrease in transmission power. Background is given on distributed convex optimization and acoustic beamforming. The new model is described with analysis of its behaviour under independent noise. Simulation results demonstrate the desirable properties of the new model in comparison with centralized output computation.</p>

Convex Optimization for Distributed Acoustic Beamforming

<p>Beamforming filter optimization can be performed over a distributed wireless sensor network, but the output calculation remains either centralized or linked in time to the weights optimization. We propose a distributed method for calculating the beamformer output which is independent of the filter optimization. The new method trades a small decrease in signal to noise performance for a large decrease in transmission power. Background is given on distributed convex optimization and acoustic beamforming. The new model is described with analysis of its behaviour under independent noise. Simulation results demonstrate the desirable properties of the new model in comparison with centralized output computation.</p>

Distributed convex optimization via proportional-integral-differential algorithm

This paper studies the distributed convex optimization problem, where the global utility function is the sum of local cost functions associated to the individual agents. Only using the local information, a novel continuous-time distributed algorithm based on proportional-integral-differential (PID) control strategy is proposed. Under the assumption that the global utility function is strictly convex and local utility functions have locally Lipschitz gradients, the exponential convergence of the proposed algorithm is established with undirected and connected graph among these agents. Finally, numerical simulations are presented to illustrate the effectiveness of theoretical results.