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Secure State Estimation with Asynchronous Measurements against Malicious Measurement-Data and Time-Stamp Manipulation
Tsinghua University, Department of Automation, Beijing, China.
Uppsala University, Department of Information Technology, Uppsala, Sweden.
Uppsala University, Department of Information Technology, Uppsala, Sweden.
Tsinghua University, Department of Automation, Beijing, China.
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2023 (English)In: 2023 62nd IEEE Conference on Decision and Control, CDC 2023, Institute of Electrical and Electronics Engineers (IEEE) , 2023, p. 7073-7080Conference paper, Published paper (Refereed)
Abstract [en]

This paper proposes a secure state estimation scheme with asynchronous non-periodic measurements for con-tinuous LTI systems under false data attacks on measurement transmission channels. Each sensor transmits the measurement information in a triple comprised of its sensor index, the time-stamp, and the measurement value to the fusion center via unprotected communication channels. A malicious attacker can corrupt a subset of sensors by (i) manipulating the time-stamp and the measurement value, (ii) blocking transmitted measurement triples, or (iii) injecting fake measurement triples. To deal with such attacks, we propose a secure state estimator by designing decentralized local estimators and fusing all the local states by the median operator. The local estimators receive the sampled measurements and update their local state in an asynchronous manner, while the fusion center triggers the fusion and generates a secure estimation in the presence of a local update. We prove that local estimators of benign sensors are unbiased with stable error covariance. Moreover, the fused secure estimation error has bounded expectation and covariance against at most p corrupted sensors as long as the system is 2p-sparse observable. The efficacy of the proposed scheme is demonstrated through a benchmark example of the IEEE 14-bus system.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2023. p. 7073-7080
Series
Proceedings of the IEEE Conference on Decision and Control, ISSN 0743-1546
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-343709DOI: 10.1109/CDC49753.2023.10383571ISI: 001166433805126Scopus ID: 2-s2.0-85184795512OAI: oai:DiVA.org:kth-343709DiVA, id: diva2:1839904
Conference
62nd IEEE Conference on Decision and Control, CDC 2023, Singapore, Singapore, Dec 13 2023 - Dec 15 2023
Note

QC 20240226

Part of ISBN 979-8-3503-0124-3

Available from: 2024-02-22 Created: 2024-02-22 Last updated: 2024-03-26Bibliographically approved

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Johansson, Karl H.

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