A Study of Multi-Step Sparse Vessel Trajectory Restoration Based on Feature Correlation

Abstract

To address the issue of data integrity and reliability caused by sparse vessel trajectory data, this paper proposes a multi-step restoration method for sparse vessel trajectory based on feature correlation. First, we preserved the overall trend of the trajectory by detecting and marking the sparse and abnormal vessel trajectories points and using the cubic spline interpolation method for preliminary restoration. Then, we established a composite indicator of feature correlation for selecting highly correlated trajectory features as inputs to the model, reducing data redundancy while improving the key correlation between trajectory features. Finally, we developed a vessel trajectory restoration model based on the Seq2Seq network for secondary restoration of the trajectory to ensure the accurate restoration of the vessel trajectory. Through comparison and ablation experiments, we demonstrate that the method can efficiently extract highly correlated features from vessel trajectories and combines the advantages of the interpolation method and neural network model to effectively improve the accuracy of trajectory restoration and ensure the integrity and accuracy of trajectory data. The research results could provide crucial technical support for the subsequent mining of vessel behavior patterns and assisted decision-making, which holds significant application prospects and potential value.