Partial diffusion Kalman filtering for distributed state estimation in multiagent networks

Vahidpour, V., Rastegarnia, A., Khalili, A. and Sanei, S. ORCID: 0000-0002-3437-2801, 2019. Partial diffusion Kalman filtering for distributed state estimation in multiagent networks. IEEE Transactions on Neural Networks and Learning Systems. ISSN 2162-237X

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Abstract

Many problems in multiagent networks can be solved through distributed learning (state estimation) of linear dynamical systems. In this paper, we develop a partial-diffusion Kalman filtering (PDKF) algorithm, as a fully distributed solution for state estimation in the multiagent networks with limited communication resources. In the PDKF algorithm, every agent (node) is allowed to share only a subset of its intermediate estimate vectors with its neighbors at each iteration, reducing the amount of internode communications. We analyze the stability of the PDKF algorithm and show that the algorithm is stable and convergent in both mean and mean-square senses. We also derive a closed-form expression for the steady-state mean-square deviation criterion. Furthermore, we show theoretically and by numerical examples that the PDKF algorithm provides a trade-off between the estimation performance and the communication cost that is extremely profitable.

Item Type: Journal article
Publication Title: IEEE Transactions on Neural Networks and Learning Systems
Creators: Vahidpour, V., Rastegarnia, A., Khalili, A. and Sanei, S.
Publisher: Institute of Electrical and Electronics Engineers
Date: 11 March 2019
ISSN: 2162-237X
Identifiers:
NumberType
10.1109/tnnls.2019.2899052DOI
Divisions: Schools > School of Science and Technology
Depositing User: Jonathan Gallacher
Date Added: 18 Mar 2019 12:57
Last Modified: 18 Mar 2019 12:57
URI: http://irep.ntu.ac.uk/id/eprint/36066

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