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Statistical Linear Regression Approach to Kalman Filtering and Smoothing under Cyber-Attacks

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A4 Artikkeli konferenssijulkaisussa

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en

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5

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2025 33rd European Signal Processing Conference, EUSIPCO 2025 - Proceedings, pp. 2547-2551

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Remote state estimation in cyber-physical systems is often vulnerable to cyber-attacks due to wireless connections between sensors and computing units. In such scenarios, adversaries compromise the system by injecting false data or blocking measurement transmissions via denial-of-service attacks, distorting sensor readings. This paper develops a Kalman filter and Rauch-Tung-Striebel (RTS) smoother for linear stochastic state-space models subject to cyber-attacked measurements. We approximate the faulty measurement model via generalized statistical linear regression (GSLR). The GSLR-based approximated measurement model is then used to develop a Kalman filter and RTS smoother for the problem. The effectiveness of the proposed algorithms under cyber-attacks is demonstrated through a simulated aircraft tracking experiment.

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Publisher Copyright: © 2025 European Signal Processing Conference, EUSIPCO. All rights reserved.

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Kumar, K, Iqbal, M & Särkkä, S 2025, Statistical Linear Regression Approach to Kalman Filtering and Smoothing under Cyber-Attacks. in 2025 33rd European Signal Processing Conference, EUSIPCO 2025 - Proceedings. European Association For Signal and Image Processing, pp. 2547-2551, European Signal Processing Conference, Palermo, Italy, 08/09/2025. https://doi.org/10.23919/EUSIPCO63237.2025.11226213

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