For automotive radar-based extended object tracking, this paper proposes a new approach, which jointly estimates the kinematic state and the extension of a vehicle. The vehicle’s shape is described as a rectangle with its vertices treated as the extension state. “Having a rectangular shape” is described as a quadratic equality constraint on the state. To deal with the challenging problem of modeling measurement (scattering center) distribution over a vehicle, we partition the rectangular area into multiple regions and assume that in each region the scattering centers have a simple distribution. An approach is proposed to associate measurements with these regions. Given an association, the target state is estimated in a linear minimum mean-square-error framework with the shape constraint treated as a pseudo measurement. The probabilities for these regions to generate measurements are updated online. The estimate is then projected into the constraint space. The effectiveness of the proposed approach is illustrated using both simulated and real data.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Automotive Radar-Based Vehicle Tracking Using Data-Region Association


    Beteiligte:
    Cao, Xiaomeng (Autor:in) / Lan, Jian (Autor:in) / Li, X. Rong (Autor:in) / Liu, Yu (Autor:in)


    Erscheinungsdatum :

    2022-07-01


    Format / Umfang :

    2774050 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





    Dynamic Vehicle Perception Using Automotive Radar

    Du, Kangjian / Wu, Yaoran / Li, Xiying et al. | ASCE | 2020


    Dynamic Vehicle Perception Using Automotive Radar

    Du, Kangjian / Wu, Yaoran / Li, Xiying et al. | TIBKAT | 2021