In this a paper, an auto-tuning Extend Kalman filter (EKF) approach is developed. The objective is to design an algorithm to find the optimal values of the covariance matrices Q and R. Manual tuning of those parameters is hard and time-consuming. Besides, wrong combinations of their values can lead to filter divergence and inconsistency. The proposed approach combines several metrics derived from the filter requirements especially the filter consistency. A weighted cost function is established based on the defined metrics. The approach effectiveness is tested and verified on sensor fusion problems for drone indoor localization where good results are achieved using five (5) different numerical optimization solvers.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Auto-tuning extended Kalman filters to improve state estimation


    Beteiligte:
    Boulkroune, Boulaid (Autor:in) / Geebelen, Kurt (Autor:in) / Wan, Jia (Autor:in) / van Nunen, Ellen (Autor:in)


    Erscheinungsdatum :

    04.06.2023


    Format / Umfang :

    1461273 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Tuning of Extended Kalman Filters for Sensorless Motion Control with Induction Motor

    Alonge, Francesco / D'Ippolito, Filippo / Fagiolini, Adriano et al. | IEEE | 2019


    Vehicle state estimation based on Kalman filters

    Bersani, M. / Vignati, M. / Mentasti, S. et al. | IEEE | 2019


    Extended Kalman Filter for MMS State Estimation

    Markley, F. Landis / Harman, Richard R. / Thienel, Julie K. | NTRS | 2009


    Sensitivity Analysis of Extended and Unscented Kalman Filters for Attitude Estimation

    Rhudy, Matthew / Gu, Yu / Gross, Jason et al. | AIAA | 2013