Distributed State Estimation for Mixed Delays System Over Sensor Networks With Multichannel Random Attacks and Markov Switching Topology

• Published in: IEEE transaction on neural networks and learning systems 2024-06, Vol.35 (6), p.8623-8637
• Main Authors: Qian, Wei, Lu, Di, Guo, Simeng, Zhao, Yunji
• Format: Article
• Language: English
• Subjects: Algorithms; Channel estimation; Delay systems; Distributed state estimation; Markov chains; Markov processes; Markov switching topology; multichannel random attack; Network topologies; Network topology; Parameter estimation; sensor network; State estimation; Stochasticity; Switches; Topology
• ISSN: 2162-237X; 2162-2388
• DOI: 10.1109/TNNLS.2022.3230978

This article deals with the distributed state estimation for mixed delays system under unknown attacks. A new multichannel random attack model is established for the first time, where network attacks are considered to exist in three channels: the target-to-sensor channel, the senor-to-sensor channel, and the sensor-to-estimator channel. In the above model, transmitted packets are allowed to be attacked multiple times simultaneously, and when they are successfully attacked, the transmitted information is modified. Besides, the topology of the sensor network is considered to change dynamically according to the Markov chain. Based on the newly established distributed estimation model, the estimation error system is proven to be asymptotically mean-square stable under a given {H_{\infty} } antidisturbance index by using a Lyapunov theory and a stochastic analysis technique; then, the estimator parameter matrices are solved utilizing a linearization method. Finally, several simulation examples are listed to testify the effectiveness of the designed algorithm.