基于数据挖掘和海流要素的船舶导航改进研究  

Research on ship navigation improvement based on data mining and ocean current elements

作　　者：苏娜[1] 李晓岩[1] SU Na;LI Xiao-yan(Qingdao Huanghai University,Qingdao 266555,China)

机构地区：[1]青岛黄海学院,山东青岛266555

出　　处：《舰船科学技术》2022年第11期169-172,共4页Ship Science and Technology

摘　　要：研究数据挖掘和海流要素的船舶导航改进方法,改善由于未考虑海流对船舶航行的影响,导致船舶导航应用性较差的缺陷。利用K-means聚类算法挖掘大型海洋环境数据库中的海流要素。建立船舶导航路径规划全局坐标系的环境感知模型,依据所建立环境感知模型确定船舶期望速度以及船舶转角,确定船舶导航的期望位姿。为了提升船舶导航性能,利用船舶定位校正方法改进船舶导航。船舶定位校正方法利用数据挖掘获取的海流要素,与船舶经纬度结合,校正船舶定位,利用校正后船舶定位结果实现船舶导航改进。The ship navigation improvement method of data mining and ocean current elements is studied,and the defect of poor ship navigation applicability is caused by not considering the influence of ocean current on ship navigation.The K-means clustering algorithm is used to mine ocean current elements in a large marine environment database.The environment perception model of the global coordinate system of ship navigation path planning is established,and the expected speed of the ship and the ship’s turning angle are determined according to the established environment perception model,and the desired position and attitude of the ship’s navigation is determined.In order to improve the ship navigation performance,the ship positioning correction method is used to improve the ship navigation.The ship positioning correction method uses the current elements obtained by data mining,combined with the longitude and latitude of the ship,to correct the ship positioning,and uses the corrected ship positioning results to improve the ship navigation.

关 键 词：数据挖掘 海流要素 船舶导航 K-MEANS 环境感知模型 船舶定位校正 

分 类 号：U661[交通运输工程—船舶及航道工程]