TY - GEN
T1 - Reaching Bayesian belief over networks in the presence of communication noise
AU - Wang, Yunlong
AU - Djuric, Petar M.
PY - 2013/12/1
Y1 - 2013/12/1
N2 - In this paper, we consider the problem of distributed sequential estimation in a network whose communication channels are affected by additive Gaussian noise. We propose a method that is based on cooperation among neighboring agents and that allows every agent to reach the belief that is the optimal Bayesian solution. This solution is the posterior distribution of the unknowns that is held by a fictitious fusion center. The agents, however, do not implement the Bayes' rule. Compared with the standard average consensus algorithm, the proposed method is stable in the sense that the effects of the noise do not accumulate with time and a random walk behavior is avoided. We show that with the proposed method every agent's belief converges to the belief of a fictitious fusion center, if the variance of the communication noise is bounded. We provide computer simulations that compare the proposed method with a method which works well in the noise-free case.
AB - In this paper, we consider the problem of distributed sequential estimation in a network whose communication channels are affected by additive Gaussian noise. We propose a method that is based on cooperation among neighboring agents and that allows every agent to reach the belief that is the optimal Bayesian solution. This solution is the posterior distribution of the unknowns that is held by a fictitious fusion center. The agents, however, do not implement the Bayes' rule. Compared with the standard average consensus algorithm, the proposed method is stable in the sense that the effects of the noise do not accumulate with time and a random walk behavior is avoided. We show that with the proposed method every agent's belief converges to the belief of a fictitious fusion center, if the variance of the communication noise is bounded. We provide computer simulations that compare the proposed method with a method which works well in the noise-free case.
KW - Additive noise
KW - Bayesian belief
KW - Consensus algorithm
KW - Distributed estimation
UR - http://www.scopus.com/inward/record.url?scp=84897729357&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84897729357&partnerID=8YFLogxK
U2 - 10.1109/GlobalSIP.2013.6736947
DO - 10.1109/GlobalSIP.2013.6736947
M3 - Conference contribution
AN - SCOPUS:84897729357
SN - 9781479902484
T3 - 2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings
SP - 591
EP - 594
BT - 2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings
T2 - 2013 1st IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013
Y2 - 3 December 2013 through 5 December 2013
ER -