Consensus Iterated Posterior Linearization Filter for Distributed State Estimation

作     者：Garcia-Fernandez, Angel F. Battistelli, Giorgio 

作者机构：Univ Liverpool Dept Elect Engn & Elect Liverpool L69 3GJ England Univ Firenze Dipartimento Ingn Informaz DINFO I-50139 Florence Italy 

出 版 物：《IEEE SIGNAL PROCESSING LETTERS》 (IEEE Signal Process Lett)

年 卷 期：2025年第32卷

页      面：561-565页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 

基　　金：Royal Society International Exchanges Award [IES\R1\231122] 

主　　题：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 

摘      要：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 conditional mean 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.