Marine Trajectory Reconstruction Method Based on Navigation State Recognition and Bi-Directional Kinematic Interpolation

The trajectory data mining and analysis of maritime targets are of great significance in furthering the construction of maritime traffic facilities, improving the ability of marine supervision and maintaining national marine security. However, due to factors such as detection means and environmental interference, a large number of trajectory data have problems such as large space-time span, uneven sampling, and poor continuity, which seriously restrict the effect of trajectory mining. Therefore, this paper proposes a method of trajectory reconstruction based on navigation state recognition and bidirectional kinematic interpolation. The method mainly includes three steps: (1) data preprocessing, (2) navigation state recognition, and (3) trajectory interpolation. The method can recognize the navigation state of the targets in different segments, and then adaptively select the interpolation method to reconstruct the trajectories, that is, linear interpolation in the straight segments and bidirectional kinematic interpolation in the turning segments. Among them, bidirectional kinematic interpolation uses the cubic Hermite function to nonlinearly fit the acceleration of the interpolation section, and then calculates the velocity and coordinates of the interpolation points by time weighting from the positive and negative directions. The proposed method is verified and analyzed on the contest dataset of “Intelligent classification and recognition of XX trajectories”. Compared with the existing methods, the reconstruction results of the proposed method are closer to the real trajectories, and it can effectively reconstruct the target trajectories with better accuracy and stability. At the same time, the effect of trajectories classification based on the Long Short-Term Memory (LSTM) model, which uses trajectories before and after reconstruction, is compared and analyzed. The results show that the model has a higher classification accuracy for reconstructed trajectory, which proves the necessity of trajectory reconstruction.