基于轨迹点聚类的航路发现方法  

Route discovery method based on trajectory point clustering

作　　者：刘海杨 孟令航 林仲航 谷源涛[1] LIU Haiyang;MENG Linghang;LIN Zhonghang;GU Yuantao(Department of Electronic Engineering,Tsinghua University,Beijing 100084,China)

机构地区：[1]清华大学电子工程系,北京100084

出　　处：《计算机应用》2022年第3期890-894,共5页journal of Computer Applications

摘　　要：为了加强对局部空域航路的掌握和管理,提出一种基于轨迹点聚类的航路发现方法。首先,针对根据真实数据的分布特点生成的仿真数据,采用预处理模块对轨迹数据的噪声进行削弱和剔除;其次,提出一种包括孤立点剔除、轨迹重采样、轨迹点聚类、聚类中心修正和连接聚类中心五个部分的航路发现方法,对航路进行提取;最后,对航路提取结果进行了可视化输出,并使用民航数据对该方法进行了验证。在仿真数据上的实验结果表明,在噪声强度为0.1°、缓冲区为30 km的条件下,所提方法的节点覆盖率和长度覆盖率分别为99%和94%;与栅格化方法相比,该方法具有较高准确性,能够对航路进行更有效的提取,达到了提取飞行器常见航路的目的。To strengthen the control and management of local airspace routes,a route discovery method based on trajectory point clustering was proposed.Firstly,for the simulation data generated according to the distribution characteristics of the real data,the pre-processing module was used to weaken and remove the noise of the trajectory data.Secondly,a route discovery method including outlier elimination,trajectory resampling,trajectory point clustering,clustering center correction,and connecting clustering centers was proposed to extract the routes.Finally,the result of route extraction was visualized and the proposed method was validated using civil aviation data.The experimental results on the simulated data show that the node coverage and the length coverage of the proposed method is 99% and 94% respectively,under the noise intensity of 0.1° and the buffer area of 30 km.Compared with the rasterization method,the proposed method has higher accuracy and can extract the routes more effectively,achieving the purpose of extracting the common routes of aircraft.

关 键 词：航路发现 轨迹点聚类 机器学习 轨迹预处理 轨迹数据 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]