火力与指挥控制2024，Vol.49Issue(4)：156-163,8.DOI:10.3969/j.issn.1002-0640.2024.04.024
基于大数据的船舶活动轨迹规律挖掘方法
Mining Method of Ship Activity Trajectory Pattern Based on Big Data
安健鹏 1李海霞 1雷亚丽 1王永权 1姚陈芳2
作者信息
- 1. 北方自动控制技术研究所,太原 030006
- 2. 战略支援部队中部预备役信息通信大队,太原 030000
- 折叠
摘要
Abstract
Aiming at the problem of single feature attribute research in current ship trajectory clustering technology,a multi-dimensional ship trajectory clustering algorithm based on merge distance is proposed.By adding multiple attribute features and adopting a new distance measurement algorithm,this technique provides a new solution idea from two aspects of time sequence and complexity.On the basis of clustering results,a typical trajectory algorithm based on local regional mean is proposed to solve the problem of the lack of trajectory feature characterization methods.By calculating the mean value of each attribute,the trajectory features in the same trajectory clustering are depicted in detail.关键词
船舶轨迹聚类/相似性度量/典型轨迹提取/轨迹规律挖掘Key words
ship trajectory clustering/similarity measurement/typical trajectory extraction/trajec-tory pattern mining分类
信息技术与安全科学引用本文复制引用
安健鹏,李海霞,雷亚丽,王永权,姚陈芳..基于大数据的船舶活动轨迹规律挖掘方法[J].火力与指挥控制,2024,49(4):156-163,8.
