Distributed fusion filtering for multi-rate nonlinear systems with random sensor failures under event-triggering round-robin-like scheme

The distributed fusion filtering problem is addressed for the multi-rate nonlinear systems with random sensor failures (RSFs) over sensor networks, where a prediction compensation approach is proposed to transform the system unlike the lifting technique. The RSFs are portrayed by using stochastic variables with known statistical properties that satisfy certain probability distribution. In order to prevent data conflicts and reduce unnecessary data transmission, the event-triggering round-robin-like scheme (ETRRLS) is introduced to schedule the data transmission among sensor nodes. The main objectives of this paper are to design a local distributed filtering scheme based on the information of itself and ETRRLS scheduled neighboring nodes, and obtain an upper bound on the local filtering error (LFE) covariance which is minimized based on the filter gains design. Afterward, the local filters are fused by using the sequential covariance intersection fusion criterion. Moreover, we provide a sufficient condition, which can ensure the boundedness of the trace of LFE covariance. Finally, a simulation example is presented to illustrate the effectiveness and superiority of the newly proposed distributed fusion estimation algorithm.