Distributed state estimation through co‐acting Kalman filters

Kanagala, S. B. and Detroja, K. P. (2020) Distributed state estimation through co‐acting Kalman filters. Asian Journal of Control. ISSN 1561-8625

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Distributing calculations of a central Kalman filter requires subsystem level expressions for the propagation and update steps of the Kalman filter. It is difficult to obtain subsystem level expressions due to the inverse term present in the update step. In this manuscript, a non-iterative way of decomposing the inverse of a matrix is presented. This decomposition allows rewriting the update equations of the Kalman filter subsystem-wise. Subsequently, a Co-acting Kalman Filter (CoKF) is proposed using these decomposed central Kalman filter equations to perform distributed state estimation. The convergence of the CoKF algorithm is established under the assumption that each subsystem is observable. Two variants of the proposed CoKF, namely (m-CoKF and p-CoKF), suitable for applications on opposite ends of computation and communication resource spectrum, are presented along with the trade-offs involved. A comparison of the proposed method with existing distributed Kalman filters is also presented. The proposed CoKF algorithm is implemented on a standard wireless sensor network example with 200 nodes. The simulation results demonstrate the accuracy of the proposed CoKF algorithm relative to the central Kalman filter. ©

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IITH Creators:
IITH CreatorsORCiD
Item Type: Article
Uncontrolled Keywords: Communication resources; Distributed Kalman filters; Distributed state estimation; Kalman filter equations; Non-iterative; Subsystem level; Trade off;Cobalt compounds; Economic and social effects; Fluorine compounds; Inverse problems; Iterative methods; Potassium compounds; Sensor nodes; State estimation
Subjects: Electrical Engineering
Divisions: Department of Electrical Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 22 Jul 2021 06:23
Last Modified: 01 Mar 2022 07:25
URI: http://raiith.iith.ac.in/id/eprint/8459
Publisher URL: http://doi.org/10.1002/asjc.2358
OA policy: https://v2.sherpa.ac.uk/id/publication/90
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