FlexRay-Protocol-Based Distributed Filtering with State Saturation and Uncertain Observations over Sensor Networks

This paper is concerned with the issue of FlexRay-protocol (FRP)-based distributed filtering (DF) for time-varying systems with state saturation and uncertain observations (UOs) over sensor networks (SNs). The UOs are characterized by a sequence of Bernoulli random variables to describe the data dropouts, where missing probability changes within uncertain range. Furthermore, for the sake of alleviating communication burden, the FRP, a mixed protocol incorporating both static and dynamic scheduling rules, is adopted to schedule the neighboring nodes in framework of SNs. The focus is on the design of an FRP-based DF algorithm with the aim to guarantee that an optimized upper bound of the filtering error covariance can be acquired at every time step. Finally, the efficacy of developed FRP-based DF strategy is illustrated through a simulation experiment.