A resilient method to nonlinear distributed filtering for multi-rate systems with integral measurements under memory-event-triggered mechanism

In this paper, the resilient distributed filtering problem is studied for time-varying nonlinear multi-rate systems (TVNMRSs) with integral measurements over sensor networks, where the lifting technology is utilized during the analysis of the TVNMRSs. In order to reduce unnecessary data transmissions, the memory-event-triggered communication mechanism (METCM) is adopted to determine whether the sensor nodes communicate with each other. The purpose of this paper is to design a resilient distributed filtering method such that, for all multi-rate sampling, integral measurements, filter gain fluctuations and METCM, an upper bound on the filtering error covariance is guaranteed and minimized subsequently by choosing the appropriate filter gains. Besides, a sufficient condition with rigorous theoretical proof is provided to discuss the uniform boundedness of the upper bound on the filtering error covariance. In the end, the simulations with comparative experiments are made to demonstrate the effectiveness of proposed resilient distributed filtering algorithm based on METCM.