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Title: | Recursive State Estimation for Discrete-Time Nonlinear Systems With Binary Sensors: A Locally Minimized Variance Approach |
Authors: | Huang, Y-B Wang, Z He, Y Wu, M |
Keywords: | binary sensors;difference equations;nonlinear systems;recursive state estimation;variance constraints |
Issue Date: | 19-Mar-2024 |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Citation: | Huang, Y.-B. et al. (2024) 'Recursive State Estimation for Discrete-Time Nonlinear Systems With Binary Sensors: A Locally Minimized Variance Approach', IEEE Transactions on Automatic Control, 0 (early access), pp. 1 - 8. doi: 10.1109/TAC.2024.3378776. |
Abstract: | In this paper, the locally-minimized-variance state estimation problem is investigated for a class of discrete-time nonlinear systems with Lipschitz nonlinearities and binary sensors. The output of each binary sensor takes two possible values (e.g. 0 and 1) in accordance with whether the sensed variable surpasses a prescribed threshold or not. The purpose of this paper is to design a state estimation algorithm such that an upper bound of the estimation error covariance is firstly guaranteed and then minimized at each sampling instant by properly designing the estimator gain. The valid information of sensed variables is extracted from binary measurements, and a novel state estimator is constructed in a recursive form, which is suitable for online computations. Moreover, a sufficient condition is established to ensure the exponential boundedness of the prediction error in the mean square sense. Finally, two examples are presented to verify the effectiveness of the proposed method. |
URI: | https://bura.brunel.ac.uk/handle/2438/28753 |
DOI: | https://doi.org/10.1109/TAC.2024.3378776 |
ISSN: | 0018-9286 |
Other Identifiers: | ORCiD: Yi-Bo Huang https://orcid.org/0000-0002-9163-6850 ORCiD: Zidong Wang https://orcid.org/0000-0002-9576-7401 ORCiD: Yong He https://orcid.org/0000-0001-5691-9663 ORCiD: Min Wu https://orcid.org/0000-0002-0668-8315 |
Appears in Collections: | Dept of Computer Science Research Papers |
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