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A multi-sensor track association algorithm based on entropy function of association degree

机译:基于关联度熵函数的多传感器航迹关联算法

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摘要

For the track association problem, the method based on statistics needs to assume the sensor data obey a typical statistical distribution. To avoid such a preset assumption, this paper proposed a new entropy function of association degree and a novel track association algorithm based on entropy function of association degree. The association degree of the track pair was acquired by constructing an entropy function of association degree at the sampling points, and the track pair with high association degree was judged to be associated. The algorithm is characterized by a concise ide-a, small computational load, and no requirements on the distribution of sensor data. The algorithm was implemented in the multi-sensor multi-target environment, and was compared with two other track association algorithms. The simulation results show the effectiveness and the superiority of the algorithm.
机译:对于轨迹关联问题,基于统计的方法需要假设传感器数据服从典型的统计分布。为避免这种预设,本文提出了一种新的关联度熵函数和一种基于关联度熵函数的航迹关联算法。通过在采样点上构建关联度的熵函数来获取轨道对的关联度,并判断关联度高的轨道对具有关联性。该算法的特点是思路简洁,计算量小,对传感器数据的分布无要求。该算法是在多传感器多目标环境中实现的,并与其他两种轨迹关联算法进行了比较。仿真结果表明了该算法的有效性和优越性。

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