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Unscented Tobit Kalman filtering for switched nonlinear systems with censored measurement

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  • Li, Jiajia
  • Wei, Guoliang
  • Li, Wangyan

Abstract

This paper presents a new unscented Tobit Kalman filtering (UTKF) algorithm for switched nonlinear systems with unknown modes and censored measurements. The stochastic switching is considered where the mode information cannot be accessed directly. To reflect the censoring phenomenon, a series of decision variables are introduced and the Type-II Tobit Model is used to characterize the censored measurements where measurements can be transmitted when the decision variables are greater than prescribed thresholds. The aim of this paper is to design a filtering algorithm such that unknown modes, parameters and state are estimated simultaneously over a given finite horizon. By employing the Variational Bayesian (VB) algorithm, several marginal distributions are generated to approximate the joint distribution with the unknown variables. Subsequently, by resorting to the UTKF and Bayesian inference, a new filtering algorithm is developed under the unknown modes and censored measurements. Finally, a simulation result is employed to illustrate the proposed algorithm.

Suggested Citation

  • Li, Jiajia & Wei, Guoliang & Li, Wangyan, 2022. "Unscented Tobit Kalman filtering for switched nonlinear systems with censored measurement," Applied Mathematics and Computation, Elsevier, vol. 431(C).
    • Handle: RePEc:eee:apmaco:v:431:y:2022:i:c:s0096300322004015
    DOI: 10.1016/j.amc.2022.127327
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    References listed on IDEAS

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    1. Amemiya, Takeshi, 1979. "The Estimation of a Simultaneous-Equation Tobit Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 20(1), pages 169-181, February.
    2. Richard T. Carson & Yixiao Sun, 2007. "The Tobit model with a non-zero threshold," Econometrics Journal, Royal Economic Society, vol. 10(3), pages 488-502, November.
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    Cited by:

    1. Lv, Yuan-Wei & Yang, Guang-Hong & Dimirovski, Georgi Marko, 2025. "Distributed adaptive moving horizon estimation for multi-sensor networks subject to quantization effects," Applied Mathematics and Computation, Elsevier, vol. 488(C).
    2. Chen, Xiaofeng & Lin, Dongyuan & Li, Hua & Cheng, Zhi, 2025. "Minimum error entropy high-order extend Kalman filter with fiducial points," Applied Mathematics and Computation, Elsevier, vol. 487(C).

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