基于超宽带定位的邮轮乘客车队发现算法

Sixun Yan, Bing Wu, Lei Shang, Yang Wang, Jieyin Lyu
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引用次数: 1

摘要

为了准确地发现邮轮内部空间中乘客之间的车队模式,邮轮采用超宽带定位提供船上人员定位。结合超宽带定位数据的特点和乘客定位的语义序列,提出了一种改进的基于Haussdorff-DBSCAN的邮轮乘客轨迹聚类算法。在第一阶段,传统的Haussdorff距离计算方法不能精确地表达两个轨迹之间的相似性。在计算中加入了位置语义信息,使得在发现轨迹的车队模式时更加准确。在第二阶段,改进的Hausdorff-DBSCAN算法的输入是乘客轨迹数据集,并根据轨迹的总体相似阈值确定聚类半径。输出是同一同伴组中出现的乘客轨迹集群。由于实际巡航中不具备安装超宽带定位装置的条件,通过对某一甲板上的乘客进行仿真得到的轨迹数据验证了该算法的有效性,并在Anylogic中进行了建模。结果表明,该算法的准确率达到0.92,召回率达到0.93,大大高于现有算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Convoy Discovering Algorithm for Passengers in the Cruise Based on UWB Positioning
To accurately discover the convoy pattern among passengers in the interior space of cruise, UWB positioning is employed in the cruise to provide on-board personnel positioning. An improved Haussdorff-DBSCAN based scheme is proposed to find trajectory clustering of the cruise passengers, considering the characteristics of UWB location data and the semantic sequence of passenger-wise location. In the first phase, traditional Haussdorff distance calculation method cannot precisely express the similarity between two trajectories. The location semantic information is added to the calculation so that it will be more accurate when discover the convoy pattern of trajectories. In the second phase, the input of the improved Hausdorff-DBSCAN algorithm is the passenger trajectories data set, and the clustering radius is determined according to the overall similarity threshold of trajectories. The output are the emerging clusters of passenger trajectories in the same companion group. Due to the reason that there is no condition to install UWB positioning devices in a real cruise, the validity of the presented algorithm is verified by the trajectory data obtained from the passengers simulation on one deck of the cruise, which is modeled in Anylogic. The results indicate that the precision of the algorithm reaches 0.92, the recall value reaches 0.93, substantially higher than the existing algorithm.
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