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• W4312532819 endingPage "854" @default.
• W4312532819 startingPage "844" @default.
• W4312532819 abstract "This paper addresses the distributed state estimation over a large scale sensor network wherein each sensor may have an unknown measurement bias. In order to mitigate the influence of biases to state estimation, a bias estimator only depending on the structure of local sensor is constructed to ensure the algorithm's scalability for sensor networks. It is proved that the proposed bias estimator can effectively realize the bias estimation for the slow time-varying measurement bias. In addition, a distributed Kalman filter embedded with local bias estimator is developed to efficiently correct the biased measurements. Both consistency and stability of the filter are guaranteed under a general collective observability condition for linear time-varying systems. A numerical simulation is provided to illustrate the effectiveness of the developed results." @default.
• W4312532819 created "2023-01-05" @default.
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• W4312532819 date "2022-01-01" @default.
• W4312532819 modified "2023-10-10" @default.
• W4312532819 title "Distributed Filter With Biased Measurements: A Scalable Bias-Correction Approach" @default.
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• W4312532819 doi "https://doi.org/10.1109/tsipn.2022.3210374" @default.
• W4312532819 hasPublicationYear "2022" @default.
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