多雷达目标跟踪与轨迹拟合

Georgiana Magu, R. Lucaciu
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引用次数: 2

摘要

针对多目标情况,提出了一种基于多项式拟合的数据关联跟踪算法,以提高汽车雷达传感器对目标轨迹的估计精度。该方法从笛卡尔坐标系下任意实轨迹都是多项式的假设出发,设想将卡尔曼滤波输出处得到的数据拟合为多项式。我们使用多项式拟合的方法对三个目标的轨迹进行拟合,我们在两个不同的场景下生成了三个目标的轨迹。在这两种情况下,通过数据关联后的卡尔曼滤波可以很好地跟踪目标,并通过多项式拟合对轨迹进行改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multiple Radar Targets Tracking and Trajectories Fitting
This paper proposes a data association and tracking algorithm followed by polynomial fitting, in the case of multiple radar targets to improve the accuracy of targets’ trajectories estimated by an automotive radar sensor.Starting from the conjecture that any real trajectory is polynomial in cartesian coordinates the proposed method supposes to fit the data obtained at the output of Kalman filter to a polynomial. We used the polynomial fitting method for the trajectories of three targets, which we generated in two different scenarios. In both scenarios the targets could be tracked properly using Kalman filters after data association, and the trajectories were improved by applying the polynomial fitting.
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