Distributed Estimation for Markov Jump Systems via Diffusion Strategies

Wenling Li; Yingmin Jia

doi:10.1109/TAES.2017.2650801

Distributed Estimation for Markov Jump Systems via Diffusion Strategies

Wenling Li^*
, Yingmin Jia

^*Corresponding author for this work

School of Automation Science and Electrical Engineering

Research output: Contribution to journal › Article › peer-review

46 Scopus citations

Abstract

We consider the problem of distributed estimation for Markov jump systems. A distributed interacting multiple model Kalman filter is developed based on the diffusion strategy, where the local measurements, the mode-conditioned estimates, and the likelihoods are exchanged between neighboring nodes. The proposed filter leads to stable estimates for all nodes as long as at least one node is stable in a connected network. Simulation results show that the proposed approach outperforms the existing techniques.

Original language	English
Article number	7812656
Pages (from-to)	448-460
Number of pages	13
Journal	IEEE Transactions on Aerospace and Electronic Systems
Volume	53
Issue number	1
DOIs	https://doi.org/10.1109/TAES.2017.2650801
State	Published - Feb 2017

Keywords

Distributed estimation
Markov jump linear system
diffusion network
interacting multiple model (IMM)

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10.1109/TAES.2017.2650801

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abstract = "We consider the problem of distributed estimation for Markov jump systems. A distributed interacting multiple model Kalman filter is developed based on the diffusion strategy, where the local measurements, the mode-conditioned estimates, and the likelihoods are exchanged between neighboring nodes. The proposed filter leads to stable estimates for all nodes as long as at least one node is stable in a connected network. Simulation results show that the proposed approach outperforms the existing techniques.",

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AB - We consider the problem of distributed estimation for Markov jump systems. A distributed interacting multiple model Kalman filter is developed based on the diffusion strategy, where the local measurements, the mode-conditioned estimates, and the likelihoods are exchanged between neighboring nodes. The proposed filter leads to stable estimates for all nodes as long as at least one node is stable in a connected network. Simulation results show that the proposed approach outperforms the existing techniques.

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KW - Markov jump linear system

KW - diffusion network

KW - interacting multiple model (IMM)

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Distributed Estimation for Markov Jump Systems via Diffusion Strategies

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Keywords

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