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|Title:||Receding horizon filtering for a class of discrete time-varying nonlinear systems with multiple missing measurements|
|Keywords:||Discrete time-varying systems;Multiple missing measurements;Receding horizon filtering;Stochastic nonlinear|
|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.|
|Appears in Collections:||Dept of Computer Science Research Papers|
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