CONVERGENCE OF A DISTRIBUTED PARAMETER ESTIMATOR FOR SENSOR NETWORKS WITH LOCAL AVERAGING OF THE ESTIMATES

Distributed and Collaborative Signal Processing

Přednášející: Jérémie Jakubowicz, Autoři: Pascal Bianchi, Gersende Fort, Walid Hachem, Jérémie Jakubowicz, LTCI Telecom ParisTech / CNRS, France

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.

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http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5947170

Informace o přednášce

Nahráno:	2011-05-27 10:30 - 10:50, Club B
Přidáno:	15. 6. 2011 05:52
Počet zhlédnutí:	28
Rozlišení videa:	1024x576 px, 512x288 px
Délka videa:	0:17:15
Audio stopa:	MP3 [5.82 MB], 0:17:15