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基于UWB定位的邮轮乘员伴随关系发现算法

严思迅 吴兵 商蕾 吕洁印 汪洋

严思迅, 吴兵, 商蕾, 吕洁印, 汪洋. 基于UWB定位的邮轮乘员伴随关系发现算法[J]. 交通信息与安全.
引用本文: 严思迅, 吴兵, 商蕾, 吕洁印, 汪洋. 基于UWB定位的邮轮乘员伴随关系发现算法[J]. 交通信息与安全.
YAN Sixun, WU Bing, SHANG Lei, LYU Jieyin, WANG Yang. Companion Relationship Discovering Algorithm for Passengers in the Cruise Based on UWB Positioning[J]. Journal of Transport Information and Safety.
Citation: YAN Sixun, WU Bing, SHANG Lei, LYU Jieyin, WANG Yang. Companion Relationship Discovering Algorithm for Passengers in the Cruise Based on UWB Positioning[J]. Journal of Transport Information and Safety.

基于UWB定位的邮轮乘员伴随关系发现算法

基金项目: 

工信部高技术船舶科研项目(G18473CZ06)、深圳市科技创新委员会项目(CJGJZD20200617102602006)资助

详细信息
    作者简介:

    严思迅(1997-),硕士研究生.研究方向:交通信息与安全.E-mail:yansixun123@163.com

    通讯作者:

    汪洋(1976-)博士,副研究员.研究方向:水上交通安全、事故干预与应急决策.E-mail:wangyang.itsc@whut.edu.cn

  • 中图分类号: U695.1

Companion Relationship Discovering Algorithm for Passengers in the Cruise Based on UWB Positioning

  • 摘要: 为准确发现邮轮内部空间乘客之间的伴随关系,在室内环境安装UWB定位设备开展室内人员定位实验。根据UWB定位的位置数据特点,提出结合室内位置语义的Hausdorff-DBSCAN算法以聚类邮轮乘员轨迹,并利用LSTM神经网络对疑似伴随关系对象进行相似度变化趋势的预测。传统的Hausdorff算法在计算轨迹相似度时未考虑轨迹时序一致的问题,引入位置语义序列能够较好地解决这一问题。改进后的Hausdorff-DBSCAN算法的输入为乘员轨迹数据集,根据轨迹整体相似度阈值选定聚类半径,输出具有伴随关系的乘员轨迹聚类结果; LSTM神经网络以定长时间窗口的点邻近度序列为输入,预测后一时刻点邻近度值,结合轨迹相似度阈值和预测结果分析乘员伴随关系的时序变化。利用Anylogic建模单层邮轮室内环境进行乘员仿真得到的轨迹数据验证算法的有效性。改进的Hausdorff-DBSCAN算法的准确率为0.920,召回率为0.950,F1值为0.934,准确率高出对比算法至少5.7%,召回率高出对比算法至少8.0%,F1值高出对比算法至少6.7%。同时LSTM在预测邮轮乘员之间相似度变化时,收敛后的误差值能保持在3%~4%左右,预测结果具有较高的准确性。

     

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出版历程
  • 收稿日期:  2021-07-31
  • 网络出版日期:  2021-12-14

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