Distribution function tracking filter design using hybrid characteristic functions

Jinglin Zhou, Donghua Zhou, Hong Wang, Lei Guo, Tianyou Chai

    Research output: Contribution to journalArticlepeer-review

    Abstract

    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. © 2009 Elsevier Ltd. All rights reserved.
    Original languageEnglish
    Pages (from-to)101-109
    Number of pages8
    JournalAutomatica
    Volume46
    Issue number1
    DOIs
    Publication statusPublished - Jan 2010

    Keywords

    • Characteristic functions
    • Dynamic stochastic systems
    • Hybrid random vectors
    • Non-Gaussian variables
    • Optimal filtering
    • Optimal tracking control

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