Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/10664
Title: Receding horizon filtering for a class of discrete time-varying nonlinear systems with multiple missing measurements
Authors: Ding, D
Wang, Z
Alsaadi, FE
Shen, B
Keywords: Discrete time-varying systems;Multiple missing measurements;Receding horizon filtering;Stochastic nonlinear
Issue Date: 2015
Publisher: Taylor and Francis Ltd.
Citation: International Journal of General Systems, 44(2): 198 - 211, (2015)
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.
URI: http://www.tandfonline.com/doi/abs/10.1080/03081079.2014.973732#
http://bura.brunel.ac.uk/handle/2438/10664
DOI: http://dx.doi.org/10.1080/03081079.2014.973732
ISSN: 0308-1079
1563-5104
Appears in Collections:Dept of Computer Science Research Papers

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