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.
Highlights
In recent years, wireless sensor networks (WSNs) have been widely used in various fields, such as environmental monitoring, smart grid, smart transportation, etc. [1,2,3,4,5]
More and more scholars are paying attention to the distributed filtering problem based on WSN
A distributed filtering algorithm based on Kalman consensus has been proposed in [7], which is suitable for sensor networks with time-varying transmission delays in communication links
Summary
Wireless sensor networks (WSNs) have been widely used in various fields, such as environmental monitoring, smart grid, smart transportation, etc. [1,2,3,4,5]. A distributed filtering algorithm based on Kalman consensus has been proposed in [7], which is suitable for sensor networks with time-varying transmission delays in communication links. A distributed H∞ state estimation approach has been proposed in [22,23] for discrete-time systems with random switching topologies, packet losses, and partial information exchange, where the switching topology is defined by non-homogeneous Markov chains. The development of a distributed H∞-consensus state estimation method with a time-varying switching topology subject to partially unknown transition probability is of great significance to achieve target estimation or tracking, which is the first motivation of this study. We focus on solving the distributed H∞ filtering problem for a class of discrete systems in a realistic sensor network and filtering network environment, while considering the effects of sensor quantization and switching topologies. The symbol “∗” represents a symmetric item in a matrix
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