A GMM-Based Secure State Estimation Approach against Dynamic Malicious Adversaries | Zendy

Cui Zhu | Zendy; Zile Wang | Zendy; Zeyuan Zang | Zendy; Yuxuan Li | Zendy; Huanming Zheng | Zendy

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A GMM-Based Secure State Estimation Approach against Dynamic Malicious Adversaries

Author(s) -

Cui Zhu,

Zile Wang,

Zeyuan Zang,

Yuxuan Li,

Huanming Zheng

Publication year - 2022

Publication title -

journal of sensors

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.399

H-Index - 43

eISSN - 1687-7268

pISSN - 1687-725X

DOI - 10.1155/2022/2127102

Subject(s) - gaussian , computer science , mixture model , state (computer science) , estimation , algorithm , invariant (physics) , control theory (sociology) , data mining , artificial intelligence , mathematics , control (management) , engineering , physics , systems engineering , quantum mechanics , mathematical physics

We consider the secure state estimation of linear time-invariant Gaussian systems subject to dynamic malicious attacks. An error compensator is proposed to reduce the impact of local error data on state estimation. Based on that, a new estimation algorithm based on the Gaussian mixture model (GMM) aiming at dynamic attacks is proposed, which can cluster the local state estimates autonomously and improve the remote estimation accuracy effectively. The superiority of the proposed algorithm is verified by numerical simulations.

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