A scheme for robust distributed sensor fusion based on average consensus

Lin Xiao, S. Boyd, S. Lall
IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005.  
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
more » ... hted 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. 0-7803-9202-7/05/$20.00 (C) 2005 IEEE
doi:10.1109/ipsn.2005.1440896 dblp:conf/ipsn/XiaoBL05 fatcat:pcgiwoi5uzdatmxl5t2kkwdwjy