Distributed Robust Filtering over Sensor Networks with Quantized Measurement and Switching Topologies

Abstract

This paper aims at exploring the theoretical research and distributed filtering design of state estimation for sensor networked systems with quantized measurement and switching topologies. In a sensor network, each sensor node has an independent static logarithmic quantizer function, and the quantized measurement is transmitted to the filtering network via the wireless network. In the corresponding filtering network, each local estimator achieves distributed consistent state estimation of the plant based on the local measurement and the neighboring shared information. In particular, the design of the distributed filter fully takes into account the fact that the communication links between the nodes are not fixed. That is, the communication topology has random switching, and such random switching behavior is described using Markov chains with partially unknown transition probabilities. A set of linear matrix inequalities gives the sufficient conditions for the existence of the distributed filter, while ensuring that the filter error system has the desired H∞ performance. Finally, two numerical simulations show the effectiveness of the design method.