Volume 39, Number 1, February 2021
|Page(s)||119 - 125|
|Published online||09 April 2021|
A detection and restoration approach for vessel trajectory anomalies based on AIS
School of Computer Science, Northwestern Polytechnical University, Xi'an 710072, China
2 School of Information, Xi'an University of Finance and Economics, Xi'an 710100, China
3 China Waterborne Transport Research Institute, Beijing 100088, China
In view of the continuous increase in the amount of AIS data at sea and the existence of more abnormal points, it is difficult to construct ship trajectories based on AIS data. Aiming at this problem, a new method for identifying and repairing abnormal points in trajectories only based the AIS data of the ship itself is proposed. Longitude and latitude, speed, acceleration, direction and other parameters are comprehensively used to identify and repair the abnormal points in the method proposed. Compared with the methods based on single location data, it can effectively reduce the missed judgement of outliers. Compared with the methods based on trajectories clustering to judge singular point, this method does not require the data of historical trajectories to expand the application scope. The cubic spline method is used to interpolate points for the discontinuous segments to further improve the continuity and integrity of the ship trajectory. The results of AIS data processing and analysis on ships in actual sea areas verify the feasibility and effectiveness of the proposed method.
Key words: automatic identification system (AIS) / trajectory data / anomalies detection / trajectory restoring / cubic spline
关键字 : 船舶自动识别系统 / 轨迹数据 / 异常点检测 / 轨迹修复
© 2021 Journal of Northwestern Polytechnical University. All rights reserved.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.