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Institute of Electrical and Electronics Engineers, IEEE Transactions on Signal Processing, 2(59), p. 465-478

DOI: 10.1109/tsp.2010.2091639

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Decentralized Particle Filter With Arbitrary State Decomposition

Journal article published in 2011 by Tianshi Chen, Thomas B. Schon ORCID, Henrik Ohlsson, Lennart Ljung ORCID
This paper is available in a repository.
This paper is available in a repository.

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Abstract

In this paper, a new particle filter (PF) which we refer to as the decentralized PF (DPF) is proposed. By first decomposing the state into two parts, the DPF splits the filtering problem into two nested subproblems and then handles the two nested subproblems using PFs. The DPF has the advantage over the regular PF that the DPF can increase the level of parallelism of the PF. In particular, part of the resampling in the DPF bears a parallel structure and can thus be implemented in parallel. The parallel structure of the DPF is created by decomposing the state space, differing from the parallel structure of the distributed PFs which is created by dividing the sample space. This difference results in a couple of unique features of the DPF in contrast with the existing distributed PFs. Simulation results of two examples indicate that the DPF has a potential to achieve in a shorter execution time the same level of performance as the regular PF.