Federated strong tracking filtering for nonlinear systems with multiple sensors

In this paper, the filtering problem is investigated for nonlinear systems with multiple sensors. A federated strong tracking filter is designed to track state mutations by making full use of limited sensor information. Several subsystems are composed of different sensor combinations, and their stat...

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Bibliographic Details
Published in:Transactions of the Institute of Measurement and Control 2022-12, Vol.44 (16), p.3141-3153
Main Authors: Liu, Shiyang, Gao, Ming, Huai, Wuxiang, Sheng, Li
Format: Article
Language:English
Subjects:
Mutation
Nonlinear systems
Sensors
Steering
Subsystems
Tracking filters
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Summary:In this paper, the filtering problem is investigated for nonlinear systems with multiple sensors. A federated strong tracking filter is designed to track state mutations by making full use of limited sensor information. Several subsystems are composed of different sensor combinations, and their states are independently estimated by using local filters in parallel. The strong tracking filter, as local filters, adaptively adjusts gain matrices of filters by introducing fading factors to track state mutations timely. Based on the theory of boundedness and inequality technique, the fusion estimation error is proved to be exponentially bounded in mean square. Finally, the feasibility and effectiveness of the proposed method is demonstrated by an experiment concerning the rotary steering drilling tool system.
ISSN:0142-3312
1477-0369
DOI:10.1177/01423312221099706