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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Vydáno v:Sensors (Basel, Switzerland) Ročník 25; číslo 17; s. 5396
Hlavní autoři: Wang, Yudong, Guo, Tingting, He, Xiaodong, Rong, Lihong, Li, Juan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI AG 01.09.2025
Témata:
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
recursive algorithms
joint state and fault estimation
missing measurements
measurement censoring
fusion estimation
ISSN:1424-8220, 1424-8220
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Shrnutí: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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ISSN:1424-8220
1424-8220
DOI:10.3390/s25175396