Consensus iterated posterior linearisation filter for distributed state estimation

García-Fernández, Ángel F and Battistelli, Giorgio (2025) Consensus iterated posterior linearisation filter for distributed state estimation IEEE Signal Processing Letters, 32. pp. 1-5. ISSN 1070-9908, 1558-2361

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Abstract

This paper presents the consensus iterated posterior linearisation filter (IPLF) for distributed state estimation. The consensus IPLF algorithm is based on a measurement model described by its conditionalmean and covariance given the state, and performs iterated statistical linear regressions of the measurements with respect to the current approximation of the posterior to improve estimation performance. Three variants of the algorithm are presented based on the type of consensus that is used: consensus on information, consensus on measurements, and hybrid consensus on measurements and information. Simulation results show the benefits of the proposed algorithm in distributed state estimation.

Item Type:	Article
Uncontrolled Keywords:	Covariance matrices, Approximation algorithms, Vectors, Kalman filters, Signal processing algorithms, Sensors, Consensus algorithm, State estimation, Current measurement, Noise measurement, Consensus, distributed state estimation, iterated posterior linearisation, nonlinear filtering
Divisions:	Faculty of Science & Engineering Faculty of Science & Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	08 Jan 2025 09:18
Last Modified:	28 Feb 2026 17:23
DOI:	10.1109/lsp.2025.3526092
Related Websites:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3189557
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