Distribution function tracking filter design using hybrid characteristic functions

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• 作者：Jinglin Zhou ; Donghua Zhou ; Hong Wang ; Lei Guo ; Tianyou Chai
• 关键词：Dynamic stochastic systems ; Characteristic functions ; Optimal tracking control ; Hybrid random vectors ; Non-Gaussian variables ; Optimal filtering
• 刊名：Automatica
• 出版年：2010
• 出版时间：January 2010
• 年：2010
• 卷：46
• 期：1
• 页码：101-109
• 全文大小：1866 K

文摘

A new tracking filtering algorithm for a class of multivariate dynamic stochastic systems is presented. The system is expressed by a set of time-varying discrete systems with non-Gaussian stochastic input and nonlinear output. A new concept, such as hybrid characteristic function, is introduced to describe the stochastic nature of the dynamic conditional estimation errors, where the key idea is to ensure the distribution of the conditional estimation error to follow a target distribution. For this purpose, the relationships between the hybrid characteristic functions of the multivariate stochastic input and the outputs, and the properties of the hybrid characteristic function, are established. A new performance index of the tracking filter is then constructed based on the form of the hybrid characteristic function of the conditional estimation error. An analytical solution, which guarantees the filter gain matrix to be an optimal one, is then obtained. A simulation case study is included to show the effectiveness of the proposed algorithm, and encouraging results have been obtained.