DocumentCode :
86994
Title :
Distributed particle filtering in agent networks: A survey, classification, and comparison
Author :
Hlinka, O. ; Hlawatsch, F. ; Djuric, P.M.
Author_Institution :
Inst. of Telecommun., Vienna Univ. of Technol., Vienna, Austria
Volume :
30
Issue :
1
fYear :
2013
fDate :
Jan. 2013
Firstpage :
61
Lastpage :
81
Abstract :
Distributed particle filter (DPF) algorithms are sequential state estimation algorithms that are executed by a set of agents. Some or all of the agents perform local particle filtering and interact with other agents to calculate a global state estimate. DPF algorithms are attractive for large-scale, nonlinear, and non-Gaussian distributed estimation problems that often occur in applications involving agent networks (ANs). In this article, we present a survey, classification, and comparison of various DPF approaches and algorithms available to date. Our emphasis is on decentralized ANs that do not include a central processing or control unit.
Keywords :
distributed algorithms; particle filtering (numerical methods); sequential estimation; state estimation; DPF algorithms; agent networks; distributed particle filtering; large-scale nonlinear distributed estimation problems; nonGaussian distributed estimation problems; sequential state estimation algorithms; Classification algorithms; Filters; Particle filters; Process control; State estimation;
fLanguage :
English
Journal_Title :
Signal Processing Magazine, IEEE
Publisher :
ieee
ISSN :
1053-5888
Type :
jour
DOI :
10.1109/MSP.2012.2219652
Filename :
6375933
Link To Document :
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