Distributed Sampled-Data Filtering over Sensor Networks with Markovian Switching Topologies

This paper considers a distributed sampled-data filtering problem in sensor networks with stochastically switching topologies. It is assumed that the topology switching is triggered by a Markov chain. The output measurement at each sensor is first sampled and then transmitted to the corresponding filters via a communication network. Considering the effect of a transmission delay, a distributed filter structure for each sensor is given based on the sampled data from itself and its neighbor sensor nodes. As a consequence, the distributed sampled-data filtering in sensor networks under Markovian switching topologies is transformed into mean-square stability problem of a Markovian jump error system with an interval time-varying delay. By using Lyapunov Krasovskii functional and reciprocally convex approach, a new bounded real lemma (BRL) is derived, which guarantees the mean-square stability of the error system with a desired performance. Based on this BRL, the topology-dependent sampled-data filters are obtained. An illustrative example is given to demonstrate the effectiveness of the proposed method.