Convergence of a distributed parameter estimator for sensor networks with local averaging of the estimates

The paper addresses the convergence of a decentralized Robbins-Monro algorithm for networks of agents. This algorithm combines local stochastic approximation steps for finding the root of an objective function, and a gossip step for consensus seeking between agents. We provide verifiable sufficient conditions on the stochastic approximation procedure and on the network so that the decentralized Robbins-Monro algorithm converges to a consensus. We also prove that the limit points of the algorithm correspond to the roots of the objective function. We apply our results to Maximum Likelihood estimation in sensor networks.

• Type: Journal
• Year: 2011
• Where: ICASSP
• Authors: Pascal Bianchi, Gersende Fort, Walid Hachem, Jérémie Jakubowicz
• Keywords: Decentralized Robbins-monro Algorithm, ICASSP 2011, Objective Function, Signal Processing, Stochastic Approximation