Various cyberattacks have been aimed at the Internet of Vehicles (IoV), so secure pose estimation has become an essential problem for ground vehicles. This chapter presents a pose estimation approach for ground vehicles under randomly occurring deception attacks. By modeling attacks as signals added to measurements with a certain probability, the attack model is presented and incorporated into the existing process and measurement equations of ground vehicle pose estimation based on multi‐sensor fusion. An unscented Kalman filter (UKF)‐based secure pose estimator is then proposed to generate a stable estimate of the vehicle pose states: i.e. an upper bound for the estimation error covariance is guaranteed. Finally, the simulation and experiments are conducted on a simple but effective single‐input‐single‐output dynamic system and the ground vehicle model to show the effectiveness of UKF‐based secure pose estimation. In particular, the proposed scheme outperforms the conventional KF, not only by resulting in more accurate estimation but also by providing a theoretically proven upper bound of error covariance matrices that could be used as an indication of the estimator's status.


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    Titel :

    UKF‐Based Vehicle Pose Estimation under Randomly Occurring Deception Attacks


    Beteiligte:
    Liu, Xinghua (Autor:in) / Jiang, Rui (Autor:in) / Chen, Badong (Autor:in) / Sam Ge, Shuzhi (Autor:in)


    Erscheinungsdatum :

    2022-09-27


    Format / Umfang :

    17 pages




    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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