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Nonlinear sequential fusion estimation for clustered sensor networks
Author(s) -
Zhou Kang,
Teng You,
Zhang Dan,
Zhang WenAn
Publication year - 2020
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1002/asjc.1997
Subject(s) - weighting , kalman filter , sensor fusion , nonlinear system , asynchronous communication , fusion , computational complexity theory , computer science , algorithm , estimator , control theory (sociology) , mathematical optimization , artificial intelligence , mathematics , medicine , computer network , linguistics , philosophy , physics , statistics , control (management) , quantum mechanics , radiology
This paper is concerned with distributed fusion estimation problem for discrete‐time nonlinear systems with asynchronous sampling data in the clustered sensor networks. A nonlinear sequential fusion method consisting of a sequential measurement fusion (SMF) method and a sequential state fusion (SSF) method is proposed. It is shown that the SMF estimator using the Unscented Kalman Filtering (UKF) method can handle the asynchronous measurement data sequentially and the SSF estimator which uses sequential matrix weighting method can get a close performance as the centralized batch matrix weighting method but has a lower computational complexity. The proposed measurement fusion method is able to deal with asynchronous measurements and has lower computational complexity as compared with the augmentation method. A simulation example shows the effectiveness of the proposed nonlinear sequential fusion method.