Ship trajectory clustering based on improved Hausdorff distance

Abstract

The traditional Hausdorff distance only considers the position information when calculating the similarity of ship trajectory, and rarely considers the characteristic information such as ship heading and speed. Secondly, the traditional Hausdorff distance is easily affected by the loss of track points when measuring ship trajectory, resulting in the increase of the distance between ship trajectories. In order to measure the distance between ship trajectories more accurately, this paper proposed a trajectory similarity measurement method based on improved Hausdorff distance. The algorithm took two important behavioral characteristics of ship heading and speed into account. It makes the distance measurement between ship trajectories more accurate and solves the problem that the distance between ship trajectories increases due to the loss of ship trajectory points in the traditional Hausdorff distance measurement. The ship trajectory data from Shanghai South Channel are used in the experiment. Compared with the traditional hausdorff distance, the results show that the clustering results of the proposed method can effectively distinguish the differences in the behavior characteristics of the trajectory