In this study, an enhanced approach for automotive radar systems is proposed to solve the detection, tracking, and track management problem in the presence of clutter with high accuracy and low computational cost. The unscented Kalman filter (UKF) with a constant turn rate and acceleration (CTRA) dynamic model is employed for target tracking, and the tracking accuracy is enhanced by incorporating the linear regression (LR) algorithm into the UKF-CTRA algorithm. We investigate, for the first time, the Joint Probabilistic Data Association (JPDA) algorithm for data association, and the composite M/N tests for track management. The capability of the proposed approach (CTRA-UKF-LR-JPDA-composite-M/N-tests) is demonstrated by comparing it with various algorithms for different single and multi-target tracking scenarios and for various sets of parameter regimes. The results show the superior performance of the proposed method over other existing techniques in automotive radar systems. This reveals the effectiveness of the proposed algorithm as a promising technique in automotive applications.


    Access

    Access via TIB


    Export, share and cite




    Automotive Radar-Based Vehicle Tracking Using Data-Region Association

    Cao, Xiaomeng / Lan, Jian / Li, X. Rong et al. | IEEE | 2022


    Vehicle location and tracking in urban environments

    Kuehnle,A. / Daimler-Benz,Stuttgart,DE | Automotive engineering | 1991


    Automotive Radar Multipath Propagation in Uncertain Environments

    Kamann, Alexander / Held, Patrick / Perras, Florian et al. | IEEE | 2018


    Stereovision Based Vehicle Tracking in Urban Traffic Environments

    Danescu, R. / Nedevschi, S. / Meinecke, M.M. et al. | IEEE | 2007