Distributed Filtering for Uncertain Systems Under Switching Sensor Networks and Quantized Communications

TL;DR

This paper develops distributed Kalman filters for uncertain systems with switching sensor networks and quantized data, improving estimation accuracy and robustness under communication constraints.

Contribution

It introduces an extended state based distributed Kalman filter and an event-triggered version that handle biased noisy observations and quantized messages in switching networks.

Findings

The proposed filters provide online upper bounds of mean square errors.

They ensure mean square error boundedness and asymptotic unbiasedness.

Numerical simulations confirm the effectiveness of the filters.

Abstract

This paper considers the distributed filtering problem for a class of stochastic uncertain systems under quantized data flowing over switching sensor networks. Employing the biased noisy observations of the local sensor and interval-quantized messages from neighboring sensors successively, an extended state based distributed Kalman filter (DKF) is proposed for simultaneously estimating both system state and uncertain dynamics. To alleviate the effect of observation biases, an event-triggered update based DKF is presented with a tighter mean square error bound than that of the time-driven one by designing a proper threshold. Both the two DKFs are shown to provide the upper bounds of mean square errors online for each sensor. Under mild conditions on systems and networks, the mean square error boundedness and asymptotic unbiasedness for the proposed two DKFs are proved. Finally, the numerical simulations demonstrate the effectiveness of the developed filters.