Minimum entropy filtering for a single output non-Gaussian stochastic system using state transformation

Zhang, Qichun; Ding, Zhengtao; Wang, Hong

doi:10.1016/j.automatica.2024.111692

Title: Minimum entropy filtering for a single output non-Gaussian stochastic system using state transformation

Journal Article · 14 May 2024 · Automatica

DOI: https://doi.org/10.1016/j.automatica.2024.111692 · OSTI ID:2371115

Zhang, Qichun ^[1]; Ding, Zhengtao ^[2]; Wang, Hong ^[3]

Buckinghamshire New University, High Wycombe (United Kingdom)
Univ. of Manchester (United Kingdom)
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

This paper presents a novel filter design for the single-output stochastic non-linear systems subjected to non-Gaussian noises and the proposed assumptions. Based on a state transformation, the unmeasurable states of the systems can be estimated where non-linear terms in the systems have been eliminated. It has been shown that the estimation error is linearly dynamical regarding to the presented vector-valued filter gain which can be optimised by minimising the entropy-based performance criterion. In addition, the convergence of the presented algorithm is analysed in mean-square sense and a numerical example is given to verify the effectiveness of the presented filtering algorithm. Meanwhile, the extended Kalman filter, unscented particle filter and minimum entropy filter are given for the comparisons of the filtering performance. Following the presented framework, some extensions of the presented filtering algorithm are discussed to indicate the flexibility of the filter design. The contribution of this paper can be summarised as establishing a novel minimum entropy filtering framework which consists of model transformation, entropy optimisation and convergence analysis.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 2371115

Journal Information:: Automatica, Journal Name: Automatica Vol. 166; ISSN 0005-1098

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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