基于航行经验的内河稀疏AIS轨迹估计方法

黄亮; 刘益; 文元桥; 耿晓巧; 孙腾达

doi:10.16411/j.cnki.issn1006-7736.2017.03.002

大连海事大学学报 >

2017 , Vol. 43 >Issue 3: 7 - 13

DOI: https://doi.org/10.16411/j.cnki.issn1006-7736.2017.03.002

交通运输工程

基于航行经验的内河稀疏AIS轨迹估计方法

黄亮 ,
刘益 ,
文元桥 ,
耿晓巧 ,
孙腾达

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（1. 武汉理工大学航运学院，武汉 430063；2.内河航运技术湖北省重点实验室，武汉 430063;3.中国交通通信信息中心，北京 100011）

黄亮(1986-)，男，博士后，E-mail:plaquemine@whu.edu.cn.

收稿日期: 2016-12-13

修回日期: 2017-04-08

网络出版日期: 2017-04-08

基金资助

国家自然科学基金资助项目（51679180）；武汉理工大学双一流项目资助.

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Inland waterway sparse AIS trajectory estimation method based on navigation experience

HUANG Liang ,
LIU Yi ,
WEN Yuan-qiao ,
GENG Xiao-qiao ,
SUN Teng-da

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(1. School of Navigation, Wuhan University of Technology, Wuhan 430063,China; 2. Hubei Key Laboratory of Inland Shipping Technology, Wuhan 430063,China; 3. China Transport Telecommunications & Information Center, Beijing 100011,China)

Received date: 2016-12-13

Revised date: 2017-04-08

Online published: 2017-04-08

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摘要

内河航行环境中AIS信号拥塞和丢失现象严重，导致船舶AIS轨迹稀疏不连续，而传统的采用数值分析方法拟合船舶丢失运动轨迹，无法适应地形弯曲复杂的内河航道水域，容易出现穿越陆岸的问题.为此，本文提出一种基于航行经验的内河船舶轨迹估计方法，针对稀疏轨迹的相邻采样点，基于空间一致性、时间一致性和航向一致性，提取有效的他船航行轨迹，通过核密度估计方法建模航行经验—航行热度分布；然后，考虑船舶运动趋势，计算得到基于航行热度的最优路径，并将其作为近似估计轨迹.选取三种典型内河航道场景进行稀疏轨迹还原实验.结果表明，本文方法能够很好地适应内河航道的复杂地形.

关键词： 内河船舶轨迹; 自动识别系统(AIS); 航行经验; 核密度估计; 轨迹估计

本文引用格式

黄亮 , 刘益 , 文元桥 , 耿晓巧 , 孙腾达 . 基于航行经验的内河稀疏AIS轨迹估计方法[J]. 大连海事大学学报, 2017 , 43(3) : 7 -13 . DOI: 10.16411/j.cnki.issn1006-7736.2017.03.002

Abstract

In the inland navigation environment, the serious AIS signal congestion and loss result in the ship AIS trajectory to be sparse and discontinuous. Traditional numerical analysis method is used to fit the lost trajectory of ships, and it cannot adapt to the complicated inland waterways with complicated bend, and is prone to cross the land coast. In this paper, a trajectory estimation method for inland river ship was proposed based on navigation experience. Based on the spatial consistency, time consistency and heading consistency, the history similar ship trajectories were extracted for neighbor sampling points of sparse trajectory, and the navigation experience was estimated by the method of Kernel density estimation, and navigational heat distribution map was modelled. And then the optimal route was calculated as approximate estimation trajectory in consideration of ship movement trend based on navigation heat. Three typical inland river channel scenarios were selected to carry out sparse trajectory restoration experiments. Results show that the proposed method can adapt well to the complex terrain of the inland waterway.

Key words： trajectory in inland waterway; automatic identification system(AIS); navigation experience; Kernel density estimation; trajectory restoring

参考文献

［1］甄荣.基于AIS信息的港口水域船舶异常行为识别研究［D］.厦门：集美大学,2015.
ZHEN R. Research on ship anomaly behavior identification within harbour waters based on AIS data［D］.Xiamen:Jimei University,2015.(in Chinese)
［2］陈金海，陆锋，彭国均.海洋运输船舶轨迹分析研究进展［J］.中国航海,2012,35(3):53-57.
CHEN J H,LU F,PENG G J.The progress of research in maritime vessel trajectory analysis［J］.Navigation of China,2012,35(3):53-57.(in Chinese)
［3］文元桥,耿晓巧,吴定勇,等.基于AIS信息的船舶废气排放测度模型［J］.中国航海,2015,38(4):96-101.
WEN Y Q,GENG X Q,WU D Y, et al. Estimate model of ship exhaust emissions based on AIS information［J］.Navigation of China,2015,38(4):96-101.(in Chinese)
［4］LAXHAMMAR R. Anomaly detection for sea surveillance［C］//Information Fusion,2008 11th International Conference. ［S.l.］: IEEE Press,2008.
［5］PALLOTTA G, VESPE M, BRYAN K. Vessel pattern knowledge discovery from AIS data:a framework for anomaly detection and route prediction［J］.Entropy,2013, 15(6):2218-2245.
［6］SANG L Z, YAN X P,MAO Z, et al. Restoring method of vessel track based on AIS information［C］//Distributed Computing and Applications to Business, Engineering & Science(DCABES),2012 11th International Symposium. ［S.l.］: IEEE Press,2012.
［7］SANG L Z, WALL A,MAO Z, et al. A novel method for restoring the trajectory of the inland waterway ship by using AIS data［J］.Ocean Engineering,2015,110:183-194.
［8］刘立群,吴超仲,褚端峰,等.基于Vondrak滤波和三次样条插值的船舶轨迹修复研究［J］.交通信息与安全,2015,33(4):100-105.
LIU L Q,WU C Z, CHU D F,et al. A study of ship trajectory restoration based on Vondrak filtering and cubic spline interpolation［J］.Journal of Transport Information and Safety,2015,33(4):100-105.(in Chinese)
［9］蔡奉君,胡勤友,施朝健,等.一种船舶运动状态插值算法［J］.中国航海,2015,38(1):71-74.
CAI F J,HU Q Y,SHI C J, et al. An interpolation algorithm of ship motion states［J］.Navigation of China,2015,38(1):71-74.(in Chinese)
［10］段兵兵,王解先,王成.融合速度和方向信息的船舶位置高精度预报［J］.武汉大学学报(信息科学版),2015,40(3):422-426.
DUAN B B,WANG J X,WANG C. High-precision prediction of ships including speed and orientation［J］.Geomatics and Information Science of Wuhan University,2015,40(3):422-426.(in Chinese)
［11］肖帅帅.基于数值分析推算船舶航行轨迹算法的实现［J］.科技视界,2013(1):101-102.
XIAO S S.Ship navigation path algorithm based on the analysis of numerical calculation［J］.Science & Technology Vision,2013(1):101-102.(in Chinese)
［12］陈婷婷.基于模糊结构元方法的船舶航迹拟合［J］.交通科技,2015(4):162-164.
CHEN T T.Ship track fitting based on fuzzy structured element method［J］.Transportation Science & Technology,2015(4):162-164.(in Chinese)
［13］王超,纪永刚,黎明,等.一种考虑船舶航速航向的AIS航迹插值方法［J］.舰船科学技术,2015,37(4):60-64.
WANG C, JI Y G, LI M, et al. An AIS track interpolation method considering the vessel’s speed and course［J］. Ship Science and Technology, 2015,37(4):60-64.(in Chinese)
［14］陈金海,陆锋,李明晓.海上主航迹带边界统计推断与海西航路警戒区布局优化分析［J］.地球信息科学学报,2015,17(10):1196-1206.
CHEN J H,LU F,LI M X. Optimizing the precautionary area of coastal ships’ routing in Western Taiwan Strait with statistical inference method for delineating the boundary of maritime principal fairways［J］.Journal of Geo-information Science,2015,17(10):1196-1206.(in Chinese)
［15］RISTIC B, SCALA B L, MORELANDE M, et al. Statistical analysis of motion patterns in AIS data: anomaly detection and motion prediction［C］//Information Fusion,2008 11th International Conference. ［S.l.］: IEEE Press,2008.

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