Event-Triggered Distributed Cubature Kalman Filtering Algorithm With Stealthy Attacks Over Sensor Networks

• Published in: IEEE transactions on signal and information processing over networks Vol. 11; pp. 124 - 135
• Main Authors: Ma, Yinping, Ma, Zhoujian, Li, Yinya, Liang, Yuan
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Detectors; Encryption; Estimation; Event detection; Event-triggered; Information processing; Kalman filters; nonlinear distributed state estimation; Nonlinear systems; Stability criteria; stability properties; State estimation; stealthy attack detection; Technological innovation; Vectors; Wireless networks
• ISSN: 2373-776X; 2373-7778
• DOI: 10.1109/TSIPN.2025.3525977

This article investigates the security problem of distributed state estimation for nonlinear systems subject to stealthy attacks and limited energy. First, a novel detection strategy for a nonlinear information consensus filter is designed to resist the stealthy adversary which can modify the data transmitted through the wireless network. Unlike existing attack detection strategies, the proposed defense strategy is capable of simultaneously verifying the authenticity of the received local estimate and error covariance. Afterward, considering the limited communication resources, an event-triggered distributed cubature Kalman filtering algorithm with the aforementioned detection strategy is presented to fuse the local information. This algorithm can reduce communication consumptions and guarantee good estimation precision for sensor networks with stealthy attacks and limited energy. Subsequently, the stability properties of the developed nonlinear filtering algorithm are presented. Finally, two examples are given to demonstrate the effectiveness of the proposed approach.