Extended object tracking methods are often based on the assumption that the measurements are uniformly distributed on the target object. However, this assumption is often invalid for applications using automotive radar or lidar data. Instead, there is a bias towards the side of the object that is visible to the sensor. To handle this challenge, we employ a Gaussian Mixture (GM) density to model a more detailed measurement distribution across the surface and extend a recent Kalman filter based elliptic object tracker called MEM-EKF* to get a closed-form solution for the measurement update. An evaluation of the proposed approach compared with classic elliptic trackers and a recent truncation-based approach is conducted on simulated data.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Kalman Filter Based Extended Object Tracking with a Gaussian Mixture Spatial Distribution Model


    Contributors:


    Publication date :

    2021-07-11


    Size :

    1527989 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    Extended Target Tracking using a Gaussian-Mixture PHD Filter

    Granstrom, K. / Lundquist, C. / Orguner, O. | IEEE | 2012


    Vehicle Speed Estimation Using Gaussian Mixture Model and Kalman Filter

    Tayeb, Ahmed Abdulwahab / Aldhaheri, Rabah Wasel / Hanif, Muhammad Shehzad | BASE | 2021

    Free access

    Corrections on: “Extended Target Tracking Using a Gaussian-Mixture PHD Filter”

    Granstrom, Karl / Orguner, Umut / Mahler, Ronald et al. | IEEE | 2017


    Maneuvering target tracking using extended Kalman filter

    Cortina, E. / Otero, D. / D'Attellis, C.E. | IEEE | 1991