Classification Algorithm of Ship Trajectory Based on Machine Learning Techniques

Abstract

In order to solve the problem of identifying ship trajectories with different motion patterns in waterways, a ship trajectory classification algorithm based on machine learning techniques is proposed. First, the area of waterways is divided and the trajectory selection rules are formulated to construct the label matrix. Then, the piecewise cubic Hermite interpolation algorithm is used to extract the feature points of the original trajectory data from the perspectives of the equal time interval in recording time and the equal space interval in spatial distribution to construct the trajectory feature matrices. Finally, in order to train and test the classification model, the label matrix and trajectory feature matrices are put into the model and the parameters are optimized. The AIS trajectory data in Huaian section of the Beijing-Hangzhou Canal are selected for the trajectory classification experiment. In this research, the broad learning system (BLS), the back propagation neural network (BPNN) and the support vector machine (SVM) are chosen as the machine learning classification methods. The results show that the trajectory classification model based on BLS is superior to those based on BPNN and SVM in classification accuracy and training time.