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Articles

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

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Pages 198-211 | Received 10 Jun 2014, Accepted 04 Aug 2014, Published online: 26 Nov 2014
 

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.

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.

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