Joint State and Fault Estimation for Nonlinear Systems Subject to Measurement Censoring and Missing Measurements
This paper investigates the joint state and fault estimation problem for a class of nonlinear systems subject to both measurement censoring (MC) and random missing measurements (MMs). Recognizing that state estimation for nonlinear systems in complex environments is frequently compromised by MMs, MC...
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| Veröffentlicht in: | Sensors (Basel, Switzerland) Jg. 25; H. 17; S. 5396 |
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| Abstract | This paper investigates the joint state and fault estimation problem for a class of nonlinear systems subject to both measurement censoring (MC) and random missing measurements (MMs). Recognizing that state estimation for nonlinear systems in complex environments is frequently compromised by MMs, MC phenomena, and actuator faults, a novel joint estimation framework that integrates improved Tobit Kalman filtering and federated fusion is proposed, enabling simultaneous robust estimation of system states and fault signals. Among them, the Tobit measurement model is introduced to characterize the phenomenon of MC, a set of Bernoulli random variables is used to describe the MM phenomenon and common actuator faults (abrupt and ramp faults) are considered. In the fusion estimation stage, each sensor transmits observations to the local estimator for preliminary estimation, then sends the local estimated values to the fusion center for generating fusion estimates. The local filtering error covariance is ensured and the upper bound is minimized by reasonably determining the filter gain, while the fusion center performs fusion estimation based on the federated fusion criterion. In addition, this paper proves the boundedness of the filtering error of the designed estimator under certain conditions. Finally, the effectiveness of the estimation framework is demonstrated through two engineering experiments. |
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| AbstractList | This paper investigates the joint state and fault estimation problem for a class of nonlinear systems subject to both measurement censoring (MC) and random missing measurements (MMs). Recognizing that state estimation for nonlinear systems in complex environments is frequently compromised by MMs, MC phenomena, and actuator faults, a novel joint estimation framework that integrates improved Tobit Kalman filtering and federated fusion is proposed, enabling simultaneous robust estimation of system states and fault signals. Among them, the Tobit measurement model is introduced to characterize the phenomenon of MC, a set of Bernoulli random variables is used to describe the MM phenomenon and common actuator faults (abrupt and ramp faults) are considered. In the fusion estimation stage, each sensor transmits observations to the local estimator for preliminary estimation, then sends the local estimated values to the fusion center for generating fusion estimates. The local filtering error covariance is ensured and the upper bound is minimized by reasonably determining the filter gain, while the fusion center performs fusion estimation based on the federated fusion criterion. In addition, this paper proves the boundedness of the filtering error of the designed estimator under certain conditions. Finally, the effectiveness of the estimation framework is demonstrated through two engineering experiments. This paper investigates the joint state and fault estimation problem for a class of nonlinear systems subject to both measurement censoring (MC) and random missing measurements (MMs). Recognizing that state estimation for nonlinear systems in complex environments is frequently compromised by MMs, MC phenomena, and actuator faults, a novel joint estimation framework that integrates improved Tobit Kalman filtering and federated fusion is proposed, enabling simultaneous robust estimation of system states and fault signals. Among them, the Tobit measurement model is introduced to characterize the phenomenon of MC, a set of Bernoulli random variables is used to describe the MM phenomenon and common actuator faults (abrupt and ramp faults) are considered. In the fusion estimation stage, each sensor transmits observations to the local estimator for preliminary estimation, then sends the local estimated values to the fusion center for generating fusion estimates. The local filtering error covariance is ensured and the upper bound is minimized by reasonably determining the filter gain, while the fusion center performs fusion estimation based on the federated fusion criterion. In addition, this paper proves the boundedness of the filtering error of the designed estimator under certain conditions. Finally, the effectiveness of the estimation framework is demonstrated through two engineering experiments.This paper investigates the joint state and fault estimation problem for a class of nonlinear systems subject to both measurement censoring (MC) and random missing measurements (MMs). Recognizing that state estimation for nonlinear systems in complex environments is frequently compromised by MMs, MC phenomena, and actuator faults, a novel joint estimation framework that integrates improved Tobit Kalman filtering and federated fusion is proposed, enabling simultaneous robust estimation of system states and fault signals. Among them, the Tobit measurement model is introduced to characterize the phenomenon of MC, a set of Bernoulli random variables is used to describe the MM phenomenon and common actuator faults (abrupt and ramp faults) are considered. In the fusion estimation stage, each sensor transmits observations to the local estimator for preliminary estimation, then sends the local estimated values to the fusion center for generating fusion estimates. The local filtering error covariance is ensured and the upper bound is minimized by reasonably determining the filter gain, while the fusion center performs fusion estimation based on the federated fusion criterion. In addition, this paper proves the boundedness of the filtering error of the designed estimator under certain conditions. Finally, the effectiveness of the estimation framework is demonstrated through two engineering experiments. |
| Audience | Academic |
| Author | He, Xiaodong Li, Juan Rong, Lihong Guo, Tingting Wang, Yudong |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40942825$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.3390/pr12071365 10.1109/TSP.2022.3158759 10.1002/rnc.6062 10.1016/j.ijepes.2023.109721 10.1109/ChiCC.2014.6895885 10.1016/j.epsr.2024.110242 10.1016/j.inffus.2025.102955 10.1016/j.ijepes.2023.109437 10.1109/TAC.2024.3387009 10.1016/j.measurement.2025.117039 10.1109/TCST.2015.2432155 10.1016/j.inffus.2023.102121 10.3389/fnbot.2022.881021 10.1109/TAC.2020.3033710 10.1016/j.isatra.2024.11.027 10.1016/j.tsep.2025.103284 10.1109/TSP.2022.3144945 10.1109/TNNLS.2018.2885723 10.1109/JAS.2024.124338 10.1016/j.inffus.2024.102822 10.1109/7.104267 10.1016/j.omega.2024.103094 10.1016/j.inffus.2024.102859 10.1016/j.neucom.2024.127634 10.1109/ICEE-B.2017.8192072 10.1016/j.cnsns.2022.106618 10.1109/TAES.2024.3374717 10.1186/s43020-024-00149-2 10.1109/TCYB.2024.3446649 10.1016/j.conengprac.2023.105447 10.1109/JSEN.2024.3364701 10.1016/j.dsp.2023.104077 10.1002/rnc.5131 10.1109/JIOT.2023.3340415 10.1016/j.eti.2024.103625 10.1109/TIM.2023.3330214 10.1007/s12555-013-0076-y 10.1007/s00190-023-01789-z 10.1016/j.measurement.2024.115642 10.1016/j.isatra.2025.01.037 10.3390/s24061967 10.3390/s23073742 |
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| SubjectTerms | Actuators Algorithms Communication Engineering fusion estimation joint state and fault estimation Kalman filters Measurement measurement censoring Measurement techniques Methods missing measurements Navigation systems recursive algorithms Sensors |
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| Title | Joint State and Fault Estimation for Nonlinear Systems Subject to Measurement Censoring and Missing Measurements |
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