Abstract
In this paper, we mainly study the distributed estimation problem under attacks, which is mainly used to estimate the position parameters. To solve this problem, a correction-based secure diffusion least mean square (CS-dLMS) algorithm, which is a hybrid algorithm that composes of a non-cooperative LMS (nc-LMS) algorithm and a correction-based diffusion least-mean squares (C-dLMS) algorithm, is proposed for distributed estimation. The nc-LMS algorithm is used to provide a reliable reference system, which can detect reliable neighbor nodes by setting a threshold under network attacks. The correction-based least mean square algorithm can estimate unknown parameters by interacting with neighbor nodes. In order to guarantee the mean performance of the CS-dLMS algorithm under attracks, a sufficient condition is proposed. Finally, Simulation results are provided to verify the effectiveness of the proposed algorithm and it outperforms the C-dLMS algorithm and nc-LMS algorithm.
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Acknowledgments
This work was supported by the Science and Technology Major Project of Guangxi (GuikeAA18118054), NSFC (61573031, 61573059) and BJNSF (4162070).
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Chang, H., Li, W., Du, J. (2020). Correction-Based Diffusion LMS Algorithms for Secure Distributed Estimation Under Attacks. In: Jia, Y., Du, J., Zhang, W. (eds) Proceedings of 2019 Chinese Intelligent Systems Conference. CISC 2019. Lecture Notes in Electrical Engineering, vol 592. Springer, Singapore. https://doi.org/10.1007/978-981-32-9682-4_43
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DOI: https://doi.org/10.1007/978-981-32-9682-4_43
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