Decentralized optimal H∞ fusion estimation for multi-sensor networked systems with two-channel hybrid attacks

Abstract

The article aims at the decentralized optimal $H_{\infty }$ fusion estimation issue for multi-sensor networked systems with insecure network communications, where hybrid attacks consisting of stochastic deception and denial-of-service attacks happen on both the sensor-to-local filter channel and the local filter-to-fusion center channel simultaneously. Some random variables obeying Bernoulli distributions are utilized to depict the hybrid attacks existing in two classes of communication channels in a unified framework. Relying on a novel augmentation method, the fusion estimation error system with globally internal dynamics is obtained. Two sufficient conditions to assure the corresponding $H_{\infty }$ performance and exponentially mean-square stability of the local and fusion estimation error systems are derived. To reduce the adverse effect of hybrid attacks, the decentralized optimal $H_{\infty }$ fusion filter with better $H_{\infty }$ performance index than each local $H_{\infty }$ filter is presented by linear matrix inequality technique. An actual civil aircraft system demonstrates the algorithms to be valid.