Networked recursive filtering for time-delayed nonlinear stochastic systems with uniform quantisation under Round-Robin protocol

In this paper, the design of networked recursive filter is investigated for a kind of nonlinear stochastic systems subject to missing measurements, fixed time-delay and uniform quantisation under the Round-Robin protocol. In a digital platform, the information coming from multi-sensors could be subject to missing measurements due to the environment effect and then need to be transformed into digital signals via uniform quantizers. In addition, the Round-Robin protocol is adopted to govern the token accessing to channel media so as to both save energy and mitigate the data congestion. A novel extended Kalman-type recursive filter is constructed that firstly combines the Round-Robin protocol, namely, in the form of the augmentation of the plant system dynamics and the protocol-induced periodic measurements. In the simultaneous consideration of system delays, missing measurements, uniform quantisation, as well as the Round-Robin protocol, the purpose for the discussed filtering problem is to obtain a set of filter parameters over a finite-horizon to minimise the upper bound of filtering error covariance as far as possible. Via elaborate mathematical analysis, the desired filter parameter is obtained by virtue of solving two Riccati-type optimisation equations, which are dependent on the latest estimation states. The genetic algorithm has been introduced to optimise the dynamic parameter selections. In addition, it is revealed in theory that the trace of the upper bound of filtering error covariance is non-decreasing as the quantisation level increases. Finally, the effectiveness of the proposed design scheme is inspected by a discretised maneuvering target tracking system.