A detection and restoration approach for vessel trajectory anomalies based on AIS

基于AIS数据的船舶轨迹修复方法研究

¹ 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

Received: 17 July 2020

Abstract

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.

摘要

针对当前海上AIS数据量持续增加并且存在较多异常点，导致基于AIS数据的船舶轨迹构建困难，提出一种基于单船自身AIS数据进行轨迹异常点识别与修复方法。此方法充分利用AIS数据中的经纬度、速度、加速度以及航向等参数，进行轨迹异常点判定与修复，与基于单一位置数据的异常点判定与修复方法相比，能有效减少异常点的漏判，提高AIS数据的可靠性；与基于航迹聚类的异常点判定方法相比，不需要历史航迹数据支撑，拓展了使用范围。而采用三次样条方法对轨迹间的断点进行插值处理，进一步提升了轨迹数据的连续性和完整性。实际海域船舶AIS数据处理与分析结果验证表明，所提出方法具有较高可行性和有效性。