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Title: Efficient distributed state estimation of hidden Markov Models over unreliable networks
This paper presents a new recursive Hybrid consensus filter for distributed state estimation on a Hidden Markov Model (HMM), which is well suited to multirobot applications and settings. The proposed algorithm is scalable, robust to network failure and capable of handling non-Gaussian transition and observation models and is, therefore, quite general. No global knowledge of the communication network is assumed. Iterative Conservative Fusion (ICF) is used to reach consensus over potentially correlated priors, while consensus over likelihoods is handled using weights based on a Metropolis Hastings Markov Chain (MHMC). The proposed method is evaluated in a multi-agent tracking problem and a high-dimensional HMM and it is shown that its performance surpasses the competing algorithms.  more » « less
Award ID(s):
1637889 1453652 1302393
NSF-PAR ID:
10080662
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
2017 International Symposium on Multi-Robot and Multi-Agent Systems (MRS)
Page Range / eLocation ID:
112 to 119
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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