IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v516y2026ics0096300325005934.html

Maximum correntropy unscented filter for simultaneous unknown input and state estimation under non-Gaussian disturbances

Author

Listed:
  • Tan, Lei
  • Song, Xinmin
  • Zhang, Yike

Abstract

Recently, the unscented Kalman filter unbiased minimum variance-based (UKF-UMV) has been explored to address state estimation issues with unknown inputs and Gaussian noises. However, the performance of the UKF-UMV will degrade dramatically in the presence of non-Gaussian noises. To tackle this issue, this letter proposes a Gaussian kernel-based estimator called the maximum correntropy unscented simultaneous input and state estimator (MCUSISE). Firstly, the unscented transformation (UT) is applied to handle nonlinear propagation. Drawing on statistical linearization techniques (SLT), the nonlinear measurement function is converted into a linear regression equation (LRE). Then, two optimization problems based on the maximum correntropy criterion (MCC) are formulated to perform simultaneous input and state estimation (SISE). The optimal SISE is achieved by iteratively adjusting the gain matrices for input and state using fixed-point iterative algorithms (FPI). Moreover, no predefined assumptions or prior constraints are imposed on the unknown input, allowing it to take any model. Finally, simulation experiments validate that the proposed MCUSISE achieves improved performance when handling unknown inputs and non-Gaussian noises, particularly impulsive noises.

Suggested Citation

  • Tan, Lei & Song, Xinmin & Zhang, Yike, 2026. "Maximum correntropy unscented filter for simultaneous unknown input and state estimation under non-Gaussian disturbances," Applied Mathematics and Computation, Elsevier, vol. 516(C).
    • Handle: RePEc:eee:apmaco:v:516:y:2026:i:c:s0096300325005934
    DOI: 10.1016/j.amc.2025.129868
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300325005934
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.amc.2025.129868?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Li, Qian & Liu, Xinzhi & Zhu, Qingxin & Zhong, Shouming & Zhang, Dian, 2019. "Distributed state estimation for stochastic discrete-time sensor networks with redundant channels," Applied Mathematics and Computation, Elsevier, vol. 343(C), pages 230-246.
    2. Xi Liu & Badong Chen & Bin Xu & Zongze Wu & Paul Honeine, 2017. "Maximum correntropy unscented filter," International Journal of Systems Science, Taylor & Francis Journals, vol. 48(8), pages 1607-1615, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shipeng Chu & Tuqiao Zhang & Chengna Xu & Tingchao Yu & Yu Shao, 2021. "Dealing with Data Missing and Outlier to Calibrate Nodal Water Demands in Water Distribution Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(9), pages 2863-2878, July.
    2. Niu, Yichun & Gao, Ming & Sheng, Li, 2022. "Fault-tolerant state estimation for stochastic systems over sensor networks with intermittent sensor faults," Applied Mathematics and Computation, Elsevier, vol. 416(C).
    3. Quan Sun & Hong Zhang & Jianrong Zhang & Wentao Ma, 2018. "Adaptive Unscented Kalman Filter with Correntropy Loss for Robust State of Charge Estimation of Lithium-Ion Battery," Energies, MDPI, vol. 11(11), pages 1-20, November.
    4. Xie, Jiyang & Zhu, Shuqian & Zhang, Dawei, 2022. "A robust distributed secure interval observation approach for uncertain discrete-time positive systems under deception attacks," Applied Mathematics and Computation, Elsevier, vol. 413(C).
    5. Wang, Jiqiang, 2019. "Disturbance attenuation of complex dynamical systems through interaction topology design," Applied Mathematics and Computation, Elsevier, vol. 355(C), pages 576-584.
    6. 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).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:apmaco:v:516:y:2026:i:c:s0096300325005934. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/applied-mathematics-and-computation .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.