A Scheme for robust distributed sensor fusion based on average consensus

Lin Xiao, Stephen Boyd, Sanjay Lall

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1143 Scopus citations

Abstract

We consider a network of distributed sensors, where each sensor takes a linear measurement of some unknown parameters, corrupted by independent Gaussian noises. We propose a simple distributed iterative scheme, based on distributed average consensus in the network, to compute the maximum-likelihood estimate of the parameters. This scheme doesn't involve explicit point-to-point message passing or routing; instead, it diffuses information across the network by updating each node's data with a weighted average of its neighbors' data (they maintain the same data structure). At each step, every node can compute a local weighted least-squares estimate, which converges to the global maximum-likelihood solution. This scheme is robust to unreliable communication links. We show that it works in a network with dynamically changing topology, provided that the infinitely occurring communication graphs are jointly connected.

Original languageEnglish
Title of host publication2005 Fourth International Symposium on Information Processing in Sensor Networks, IPSN 2005
Pages63-70
Number of pages8
StatePublished - 2005
Externally publishedYes
Event4th International Symposium on Information Processing in Sensor Networks, IPSN 2005 - Los Angeles, CA, United States
Duration: Apr 25 2005Apr 27 2005

Publication series

Name2005 4th International Symposium on Information Processing in Sensor Networks, IPSN 2005
Volume2005

Conference

Conference4th International Symposium on Information Processing in Sensor Networks, IPSN 2005
Country/TerritoryUnited States
CityLos Angeles, CA
Period04/25/0504/27/05

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