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.
Consensus iterated posterior linearisation filter for distributed state estimation / Ángel F. García-Fernández; Giorgio Battistelli. - In: IEEE SIGNAL PROCESSING LETTERS. - ISSN 1070-9908. - ELETTRONICO. - (In corso di stampa), pp. 1-5. [10.1109/lsp.2025.3526092]
Consensus iterated posterior linearisation filter for distributed state estimation
Giorgio Battistelli
In corso di stampa
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 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.I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.