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A Novel Method for Reconstruct Ship Trajectory Using Raw AIS Data

机译：一种利用原始AIS数据重建舰船航迹的新方法

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Viewed from AIS (Automatic Identification System) data, ship trajectories comprise a non-continuous series of spatiotemporal positions. Subject to the quality of the raw data, e.g. error, anomaly, it is challenging to reconstruct an original and continuous trajectory for further safety and efficiency analysis. This paper presents a novel method to detect data anomaly, identify line type, and restore ship trajectory based on vector analysis. Ship trajectory is segmented into underway and mooring sub-trajectories by analyzing characteristics of AIS data. A base vector, which represents the trend of the trajectory, is established on the basis of position vectors. With comparison of the vectors, Anomaly data is detected and filtered. A sparse sampling technique is employed to identify the linetsype of the rest sub-trajectory. Linear interpolation and cubic spline interpolation are finally applied for straight and curve sub-trajectories respectively to reconstruct a new smooth trajectory. A case study is performed and the results indicate that the reconstructed trajectory meets the layout of fairway well, with mean errors of 2.86×10
机译：从AIS（自动识别系统）数据来看，船舶航迹包含一系列不连续的时空位置。取决于原始数据的质量，例如错误，异常，为进一步进行安全性和效率分析，重建原始且连续的轨迹具有挑战性。本文提出了一种基于矢量分析的数据异常检测，线型识别和恢复船舶航迹的新方法。通过分析AIS数据的特征，可将船舶航迹分为航行中和停泊子航迹。在位置矢量的基础上建立了代表轨迹趋势的基本矢量。通过矢量比较，可以检测并过滤异常数据。采用稀疏采样技术来识别其余子轨迹的线型。最后，将线性插值和三次样条插值分别应用于直线和曲线子轨迹，以重建新的平滑轨迹。进行了实例分析，结果表明重建的轨迹与球道布局相吻合，平均误差为2.86×10。

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