This paper presents a new decentralized Kalman filter (DKF) framework to detect and isolate faulty nodes in a network of sensors. The Chi-square test is a well-versed fault detection method. However, if error in the predicted state estimate is not within bounds defined by the predicted error covariance matrix, the Chi-square test fails. In order to detect faults (also known as outliers) in measurements, the random sample consensus (RANSAC) algorithm has been widely used in computer vision applications. We propose a novel integration of RANSAC into DKF framework, named as the DKF-RANSAC algorithm, that uses the information and information matrix of the connecting nodes to formulate a hypothesis to detect faulty nodes. An approach for minimizing the iterations in the RANSAC algorithm using the Chi-square test is also presented. The proposed DKF-RANSAC algorithm is validated on a target tracking problem. A simulation study shows that this algorithm identifies the faulty nodes correctly and isolates them, as well as handles incorrect initialization error simultaneously. The proposed DKF-RANSAC algorithm is also compared with the well-known Chi-square test as well as an adaptive DKF. (c) 2022 European Control Association. Published by Elsevier Ltd. All rights reserved.
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