Distributed sampled-data asynchronous H∞ filtering of Markovian jump linear systems over sensor networks.

This paper is concerned with distributed sampled-data asynchronous H ∞ filtering for a continuous-time Markovian jump linear system over a sensor network, where jumping instants of system modes and filter modes are asynchronous. A group of sensor nodes are deployed to measure the system's output and to collaboratively share the measurement with neighboring nodes in accordance with Markovian switching topologies. First, the measurement on each sensor node is sampled at separate discrete instants and transmitted to a remote filter through a communication network. Network-induced signal transmission delays are incorporated in data transmission channels. Second, distributed sampled-data asynchronous H ∞ filters, governed by a finite piecewise homogeneous Markov process, are delicately constructed. The resultant filtering error system is transformed into a piecewise homogeneous Markovian jump linear system with delays. Third, sufficient conditions on the existence of desired distributed sampled-data asynchronous H ∞ filters are derived such that the filtering error system is stochastically stable with the prescribed weighting average H ∞ performance. Finally, three illustrative examples are given to show the effectiveness and advantage of the proposed theoretical results.