Dynamic event-based recursive filtering for multirate systems with integral measurements over sensor networks

In this paper, the dynamic event-based recursive filtering problem is studied for multirate systems over sensor networks. The state update rate of the plant and the sampling rate of the sensors are allowed to be different in order to reflect the multirate sampling strategy. Moreover, the phenomenon of integral measurements is considered to cater for the real engineering practice. To reduce unnecessary data transmissions, the dynamic event-based mechanism is implemented in the communication channels among sensor nodes. The purpose of this paper is to design a distributed recursive filtering scheme such that, under the influence of the integral measurements, the multi-rate sampling, and the dynamic event-based mechanism, there exists a minimal upper bound on the filtering error covariance. An upper bound on the filtering error covariance is first derived by solving a matrix Riccati equation, and then minimized at each sampling instant by choosing appropriate filter gains. Comprehensive simulations are conducted on a numerical example and a practical example to show the effectiveness and superiority of the proposed dynamic event-based recursive filtering scheme.

Automated summary

This paper investigates the dynamic event-based recursive filtering problem for multirate systems over sensor networks. By considering multirate sampling strategy and integral measurements, a distributed recursive filtering scheme is implemented in communication channels among sensor nodes, achieving a minimal upper bound on the filtering error covariance.