Paper Info
Title
Decentralized State Estimation via a Hybrid of Consensus and Covariance intersection.
Abstract
This paper presents a new recursive information consensus filter for decentralized dynamic-state estimation. No structure is assumed about the topology of the network and local estimators are assumed to have access only to local information. The network need not be connected at all times. Consensus over priors which might become correlated is performed through Covariance Intersection (CI) and consensus over new information is handled using weights based on a Metropolis Hastings Markov Chains. We establish bounds for estimation performance and show that our method produces unbiased conservative estimates that are better than CI. The performance of the proposed method is evaluated and compared with competing algorithms on an atmospheric dispersion problem.
Year
Venue
Field
2016
arXiv: Systems and Control
Mathematical optimization,Metropolis–Hastings algorithm,Control theory,Markov chain,Atmospheric dispersion modeling,Covariance intersection,Prior probability,Recursion,Mathematics,Estimator
DocType
Volume
Citations 
Journal
abs/1603.00955
0
PageRank 
References 
Authors
0.34
8
3
Name
Order
Citations
PageRank
Amirhossein Tamjidi1153.25
S. Chakravorty212725.20
Dylan A. Shell333447.94