Ship behavior estimation method and software implementation based on track mining

Abstract

It is always difficult to analyze the law of ship movement, especially the behavior of ship. In this paper, a frequent pattern mining method based on a large number of historical track clustering of ships is proposed to estimate the future behavior of ships, and the software implementation is presented. In this paper, a comprehensive similarity measurement method of track is proposed and the meaning of frequent pattern mining based on track clustering is introduced. Secondly, the adaptive transformation of the classical density clustering algorithm is carried out and the implementation method of the clustering algorithm based on comprehensive similarity is given. Then, the most similar cluster of virtual trunk track calculation is extracted, and the estimation results of current ship behavior are obtained by statistics. Finally, the software design and test results based on C/S architecture are given. Experimental results show that this method can describe the behavior of track association, and the behavior estimation results obtained by the software can assist the research and judgment.

Keywords

• ship behavior estimation|frequent pattern mining|comprehensive similarity|clustering|software design