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IEEE/CAA Journal of Automatica Sinica

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Abhinoy Kumar Singh, "Major Development Under Gaussian Filtering Since Unscented Kalman Filter," IEEE/CAA J. Autom. Sinica, vol. 7, no. 5, pp. 1308-1325, Sept. 2020. doi: 10.1109/JAS.2020.1003303
Citation: Abhinoy Kumar Singh, "Major Development Under Gaussian Filtering Since Unscented Kalman Filter," IEEE/CAA J. Autom. Sinica, vol. 7, no. 5, pp. 1308-1325, Sept. 2020. doi: 10.1109/JAS.2020.1003303

Major Development Under Gaussian Filtering Since Unscented Kalman Filter

doi: 10.1109/JAS.2020.1003303
Funds:  This work was supported by INSPIRE Faculty Award, Department of Science and Technology, Government of India
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  • Filtering is a recursive estimation of hidden states of a dynamic system from noisy measurements. Such problems appear in several branches of science and technology, ranging from target tracking to biomedical monitoring. A commonly practiced approach of filtering with nonlinear systems is Gaussian filtering. The early Gaussian filters used a derivative-based implementation, and suffered from several drawbacks, such as the smoothness requirements of system models and poor stability. A derivative-free numerical approximation-based Gaussian filter, named the unscented Kalman filter (UKF), was introduced in the nineties, which offered several advantages over the derivative-based Gaussian filters. Since the proposition of UKF, derivative-free Gaussian filtering has been a highly active research area. This paper reviews significant developments made under Gaussian filtering since the proposition of UKF. The review is particularly focused on three categories of developments: i) advancing the numerical approximation methods; ii) modifying the conventional Gaussian approach to further improve the filtering performance; and iii) constrained filtering to address the problem of discrete-time formulation of process dynamics. This review highlights the computational aspect of recent developments in all three categories. The performance of various filters are analyzed by simulating them with real-life target tracking problems.

     

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    Highlights

    • This paper reviews the developments in Gaussian filtering since the unscented Kalman filter.
    • The revision is mainly focused on deterministic sigma point based Gaussian filtering.
    • The revision also includes some extensions of Gaussian filtering.
    • Discussed extensions: square-root, Gaussian-sum and continuous-discrete filtering methods.

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