Distributed Dimensionality Reduction Fusion Estimation for Stochastic Uncertain Systems With Fading Measurements Subject to Mixed Attacks

• Published in: IEEE transactions on systems, man, and cybernetics. Systems Vol. 52; no. 11; pp. 7053 - 7064
• Main Authors: Fan, Sha, Yan, Huaicheng, Zhang, Hao, Wang, Yueying, Peng, Yan, Xie, Shaorong
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Bandwidth; Covariance matrix; Dimensionality reduction; Distributed dimensionality reduction fusion estimation; DoS attacks and deception attacks; Estimation; Fading; Fading channels; fading measurements; Measurement uncertainty; Multisensor fusion; sequential intersection fusion; Stochastic processes; stochastic uncertainties; Uncertain systems
• ISSN: 2168-2216; 2168-2232
• DOI: 10.1109/TSMC.2022.3156848

In this article, the distributed fusion estimation issue with the dimensionality reduction strategy under DoS attacks and deception attacks is investigated for a class of stochastic uncertain systems with fading measurements. The stochastic uncertainties existed in the system and measurement equations are represented by state-dependent noises. The fading measurements are depicted by stochastic variables with known statistics. Then, a novel attack and compensation model is proposed to display the randomly occurring behaviors of the DoS attacks and the deception attacks within a unified framework. Furthermore, a distributed multisensor fusion estimation (DMSFE) algorithm is presented. An explicit form of dimensionality reduction is designed against attacks. Stability conditions are derived such that the mean square errors (MSEs) of the proposed DMSFE are bounded. A sequential covariance intersection fusion estimator (SCIFE) is designed to prevent the cross fusion covariance matrices calculating, which owns lower accuracy by smaller computation cost than DMSFE. An illustrative example is provided to show the effectiveness and merits of the proposed algorithm.