Kernel size plays a significant role in the performance of the maximum correntropy Kalman filter (MCC-KF). Kernel size is usually chosen by trail and error. If the kernel size is large, the MCC-KF reduces to the Kalman filter (KF). However, if the kernel size is small, the MCC-KF may diverge, or converge slowly. We propose a novel method for adaptive kernel size selection. We calculate kernel size as a weighted sum of the innovation term and the covariance of the filter-indicated estimation error at each time step. We call this filter the "MCC with adaptive kernel size filter" (MCC-AKF). We analytically prove that the true mean square error (TMSE) of the MCC-AKF is less than or equal to that of the MCC-KF under certain conditions. A simulation example is provided to illustrate the analytical results.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Maximum Correntropy Criterion Kalman Filter with Adaptive Kernel Size


    Beteiligte:
    Fakoorian, Seyed (Autor:in) / Izanloo, Reza (Autor:in) / Shamshirgaran, Azin (Autor:in) / Simon, Dan (Autor:in)


    Erscheinungsdatum :

    2019-07-01


    Format / Umfang :

    171734 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Maximum Correntropy Criterion Based Robust Kalman Filter

    Wang, Liansheng / Gao, XingWei / Yin, Lijian | British Library Conference Proceedings | 2018


    Robust Information Filter Based on Maximum Correntropy Criterion

    Wang, Yidi / Zheng, Wei / Sun, Shouming et al. | AIAA | 2016



    Adaptive robust maximum correntropy cubature Kalman filter for spacecraft attitude estimation

    Chu, Shuai / Qian, Huaming / Yan, Shuya et al. | Elsevier | 2023


    Maximum Correntropy Extended Kalman Filter for Vehicle State Observation

    Qi, Dengliang / Feng, Jingan / Ni, Xiangdong et al. | Springer Verlag | 2023