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

Abstract EN

This paper considers a distributed H∞ 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 H∞ sampled-data filtering in sensor networks under Markovian switching topologies is transformed into H∞ 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 H∞ performance.

Based on this BRL, the topology-dependent H∞ sampled-data filters are obtained.

An illustrative example is given to demonstrate the effectiveness of the proposed method.