Journal
INTERNATIONAL JOURNAL OF GENERAL SYSTEMS
Volume 44, Issue 2, Pages 198-211Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/03081079.2014.973732
Keywords
receding horizon filtering; multiple missing measurements; stochastic nonlinear; discrete time-varying systems
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Funding
- Deanship of Scientific Research (DSR) at King Abdulaziz University in Saudi Arabia [16-135-35-HiCi]
- National Natural Science Foundation of China [61329301, 61203139, 61134009, 61104125]
- Royal Society of the UK
- Shanghai Rising-Star Program of China [13QA1400100]
- Shu Guang project of Shanghai Municipal Education Commission
- Shanghai Education Development Foundation [13SG34]
- Fundamental Research Funds for the Central Universities
- DHU Distinguished Young Professor Program
- Alexander von Humboldt Foundation of Germany
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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.
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