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• W4200207236 abstract "Accurately determining a train's state is essential for passenger safety, operation efficiency, and maintenance. However, the actual operation state of a train is composed of a variety of modes and is disturbed by several known or unknown factors, for which an accurate estimator is required. Hence, in this paper, a train multi-mode model considering the actual operation environment is established, and a train state estimation method based on multi-sensor parallel fusion filter is proposed. In the parallel fusion filter, the current mode of train is determined by the proposed sliding window error and voting mechanism, and the global filter are constituted by the local filters, which are fused by linear-weighted summation. The simulation results demonstrate the effectiveness of our method in estimating the train's state. It is worth noting that even if monitoring data are missing or are abnormal, the state estimation accuracy of the proposed technique still meets the requirements of a real system, and the effectiveness and robustness of the method can be verified." @default.
• W4200207236 created "2021-12-31" @default.
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• W4200207236 date "2022-02-01" @default.
• W4200207236 modified "2023-09-26" @default.
• W4200207236 title "Multi-model train state estimation based on multi-sensor parallel fusion filtering" @default.
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• W4200207236 doi "https://doi.org/10.1016/j.aap.2021.106506" @default.
• W4200207236 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34890921" @default.
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