SuperLectures.com

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

Full Paper at IEEE Xplore

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.


  Přepis řeči

|

  Komentáře

Please sign in to post your comment!

  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