Receding horizon filtering for a class of discrete time-varying nonlinear systems with multiple missing measurements

Abstract

This paper is concerned with the receding horizon filtering problem for a class of discrete time-varying nonlinear systems with multiple missing measurements. The phenomenon of missing measurements occurs in a random way and the missing probability is governed by a set of stochastic variables obeying the given Bernoulli distribution. By exploiting the projection theory combined with stochastic analysis techniques, a Kalman-type receding horizon filter is put forward to facilitate the online applications. Furthermore, by utilizing the conditional expectation, a novel estimation scheme of state covariance matrices is proposed to guarantee the implementation of the filtering algorithm. Finally, a simulation example is provided to illustrate the effectiveness of the established filtering scheme.

Keywords:

• receding horizon filtering
• discrete time-varying systems
• stochastic nonlinear
• multiple missing measurements

Additional information

Funding

This work was supported in part by the Deanship of Scientific Research (DSR) at King Abdulaziz University in Saudi Arabia [grant number 16-135-35-HiCi]; the National Natural Science Foundation of China [grant number 61329301], [grant number 61203139], [grant number 61134009], [grant number 61104125]; Royal Society of the UK, the Shanghai Rising-Star Program of China [grant number 13QA1400100]; the Shu Guang project of Shanghai Municipal Education Commission and Shanghai Education Development Foundation [grant number 13SG34]; the Fundamental Research Funds for the Central Universities, DHU Distinguished Young Professor Program, and the Alexander von Humboldt Foundation of Germany.