Abstract
An iterated square-root cubature Kalman filter(ISRCKF) algorithm for target tracking of automotive radar is proposed in this paper, which inherits the fast and robust advantages of SRCKF, combines with Gauss-Newton iterative theory and design a algorithm to iterate update the measurement process. The filtering accuracy of target tracking algorithm of automotive radar can be further improved as the newest measurement is fully utilized. Monte-Carlo simulation experiments were carried out aim at the automotive radar target tracking problem, in which used the algorithm to compare with classical algorithms such as square-root unscented Kalman filter (SRUKF) and SRCKF. The experimental results shows that the overall filtering accuracy of this algorithm is much improved compare with other classical filtering algorithms, and the filtering accuracy can be improved with the increase of iteration number.
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