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- Title
Adaptive consensus‐based distributed state estimator for non‐linear systems in the presence of multiplicative noise.
- Authors
Keshavarz‐Mohammadiyan, Atiyeh; Khaloozadeh, Hamid
- Abstract
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.
- Publication
IET Signal Processing (Wiley-Blackwell), 2017, Vol 11, Issue 8, p986
- ISSN
1751-9675
- Publication type
Article
- DOI
10.1049/iet-spr.2017.0052