A Scheme for Robust Distributed Sensor Fusion Based on Average Consensus

Lin Xiao, Stephen Boyd, and Sanjay Lall

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.

Details

Publication typeInproceedings
Published inProceedgins of International Conference on Information Processing in Sensor Networks (IPSN)
PublisherIEEE
> Publications > A Scheme for Robust Distributed Sensor Fusion Based on Average Consensus