Event‐Triggered Distributed Fusion Estimation for Multi‐Sensor Systems Against Mixed Network Attacks
Xinran Bi, Ruozi Sun, J. Q. Li, Jun Feng Hu · International Journal of Adaptive Control and Signal Processing · 2025
ABSTRACT This paper mainly studies the distributed fusion estimation problem for multi‐sensor systems under mixed network attacks and event‐triggered mechanisms. Here, the mixed network attacks consist of random deception attacks and denial‐of‐service (DoS) attacks. Specifically, when the DoS attacks occur, the predicted values of measurements are used for compensation, which avoids the severe impact of data loss caused by attacks on estimation performance and provides an effective data fault‐tolerant mechanism for multi‐sensor systems under attacks. In this case, both deception attacks and DoS attacks are incorporated into a unified research framework, and a mixed attack model is constructed by using Bernoulli random variables, which handles the problem of modeling complexity caused by the differences in characteristics between the two types of attacks. To better schedule information, an event‐triggered mechanism is set up between the estimators and the fusion center. The timing of data transmission is determined by preset thresholds, which enable the on‐demand allocation of communication resources in an attack environment and enhance the practicality of the system. On this basis, a recursive distributed fusion estimation method is proposed by using the information transmitted by local estimation and the prior fusion estimation. The cross‐covariances about local estimations and those between local estimations and the fusion estimation can be obtained. Finally, the effectiveness of the distributed fusion estimation algorithm under mixed attacks and the event‐triggered mechanism is shown by a practical simulation experiment.