Open AccessAdaptive consensus‐based distributed state estimator for non‐linear systems in the presence of multiplicative noise
Author(s) -
KeshavarzMohammadiyan Atiyeh,
Khaloozadeh Hamid
Publication year - 2017
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
eISSN - 1751-9683
pISSN - 1751-9675
DOI - 10.1049/iet-spr.2017.0052
Subject(s) - estimator , convergence (economics) , noise (video) , computer science , consensus , control theory (sociology) , mathematics , state (computer science) , noise measurement , multiplicative function , filter (signal processing) , multiplicative noise , distributed algorithm , computational complexity theory , algorithm , mathematical optimization , multi agent system , noise reduction , artificial intelligence , statistics , transmission (telecommunications) , distributed computing , economics , image (mathematics) , computer vision , economic growth , mathematical analysis , telecommunications , control (management) , signal transfer function , analog signal
The problem of consensus‐based distributed state estimation of a non‐linear dynamical system in the presence of multiplicative observation noise is investigated in this study. Generalised extended information filter (GEIF) is developed for non‐linear state estimation in the information‐space framework. To fuse the information contribution of local estimators, an average consensus algorithm is employed. To achieve faster convergence towards consensus, a novel technique is proposed to modify the consensus weights, adaptively. Computational complexity of the proposed estimator is also analysed theoretically to demonstrate the computational advantage of the adaptive consensus‐based distributed GEIF over the centralised counterpart. Moreover, stability of local estimators in terms of mean‐square boundedness of state estimation error is guaranteed, in the presence of multiplicative noise. Simulation results are provided to evaluate performance of the proposed adaptive distributed estimator for a target‐tracking problem in a wireless sensor network.