TY - GEN
T1 - Distributed estimation using Bayesian consensus filtering
AU - Bandyopadhyay, Saptarshi
AU - Chung, Soon Jo
PY - 2014
Y1 - 2014
N2 - We present the Bayesian consensus filter (BCF) for tracking a moving target using a networked group of sensing agents and achieving consensus on the best estimate of the probability distributions of the target's states. Our BCF framework can incorporate nonlinear target dynamic models, heterogeneous nonlinear measurement models, non-Gaussian uncertainties, and higher-order moments of the locally estimated posterior probability distribution of the target's states obtained using Bayesian filters. If the agents combine their estimated posterior probability distributions using a logarithmic opinion pool, then the sum of Kullback-Leibler divergences between the consensual probability distribution and the local posterior probability distributions is minimized. Rigorous stability and convergence results for the proposed BCF algorithm with single or multiple consensus loops are presented. Communication of probability distributions and computational methods for implementing the BCF algorithm are discussed along with a numerical example.
AB - We present the Bayesian consensus filter (BCF) for tracking a moving target using a networked group of sensing agents and achieving consensus on the best estimate of the probability distributions of the target's states. Our BCF framework can incorporate nonlinear target dynamic models, heterogeneous nonlinear measurement models, non-Gaussian uncertainties, and higher-order moments of the locally estimated posterior probability distribution of the target's states obtained using Bayesian filters. If the agents combine their estimated posterior probability distributions using a logarithmic opinion pool, then the sum of Kullback-Leibler divergences between the consensual probability distribution and the local posterior probability distributions is minimized. Rigorous stability and convergence results for the proposed BCF algorithm with single or multiple consensus loops are presented. Communication of probability distributions and computational methods for implementing the BCF algorithm are discussed along with a numerical example.
KW - Estimation
KW - Multivehicle systems
KW - Networked control systems
UR - http://www.scopus.com/inward/record.url?scp=84905715153&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84905715153&partnerID=8YFLogxK
U2 - 10.1109/ACC.2014.6858896
DO - 10.1109/ACC.2014.6858896
M3 - Conference contribution
AN - SCOPUS:84905715153
SN - 9781479932726
T3 - Proceedings of the American Control Conference
SP - 634
EP - 641
BT - 2014 American Control Conference, ACC 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 American Control Conference, ACC 2014
Y2 - 4 June 2014 through 6 June 2014
ER -