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.
Automotive radar system for multiple-vehicle detection and tracking in urban environments
IET Intelligent Transport Systems ; 12 , 8 ; 783-792
2018-05-18
10 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
radar detection , joint probabilistic data association algorithm , unscented Kalman filter , probability , sensor fusion , Kalman filters , multiple-vehicle tracking , low computational cost , track management problem , radar tracking , multiple-vehicle detection , automotive radar system , UKF-CTRA algorithm , multitarget tracking , road vehicle radar , CTRA-UKF-LR-JPDA-composite-M/N-tests , single target tracking , constant turn rate and acceleration dynamic model , target tracking , tracking accuracy , linear regression algorithm , nonlinear filters , automobiles , urban environments , regression analysis
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