For the joint target tracking and classification (JTC) problem with the kinematic radar only, an improved mixture unscented Kalman filters (MUKF) algorithm is proposed. The kinematic measurements and the prior speed information envelop are used to estimate the dynamic state and classify the target. Based on the traditional mixture Kalman filters (MKF) algorithm, the MUKF algorithm adopt the unscented transform (UT) to approximate the non-linear and non-Gaussian state distribution. With the improved mutual feedback strategy, our algorithm utilizes the feedback information completely and increase the tracking efficiency on the higher probable class. Mathematical analysis and simulation results confirm the better performance of the proposed method.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    An improved mixture unscented Kalman filters algorithm for joint target tracking and classification


    Contributors:
    Zhan, Kun (author) / Xu, Long (author) / Jiang, Hong (author) / Bai, Liang (author) / Wu, Mengjie (author)


    Publication date :

    2014-08-01


    Size :

    179964 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Iterated Unscented Kalman Filter for Passive Target Tracking

    Zhan, Ronghui / Wan, Jianwei | IEEE | 2007



    A Target Tracking Method of Iterative Unscented Kalman Filter

    Chang, G. / Xu, J. / Li, A. et al. | British Library Online Contents | 2011


    Event-Triggered 2D Target Tracking Using Unscented Kalman Filter

    Sanjeevi Mitra Vemuri, V. K. / V., Subha Sree / P., Sudheesh | Springer Verlag | 2021


    An improved unscented Kalman filter for satellite tracking

    Zhu, Zhenyu / Wu, Qiong / Gao, Kun et al. | SPIE | 2018