Online Multiple Outputs Least-Squares Support Vector Regression Model of Ship Trajectory Prediction Based on Automatic Information System Data and Selection Mechanism

Abstract

Existing maritime trajectory prediction models are faced with problems of low accuracy and inability to predict ship tracks in real time. To solve the above problem, an online multiple outputs Least-Squares Support Vector Regression model based on selection mechanism was proposed: (a) converting the traditional Least-Squares Support Vector Regression’s single output to multiple outputs, aiming at the problem that the single-output of the traditional Least-Squares Support Vector Regression model is difficult to apply to complex multiple features prediction scenarios, (b) reducing the high computational complexity of matrix inversion calculations using an iterative solution, in order to solve the problem of poor real-time performance, (c) determining whether to use online model based on the characteristics of different trajectories, and (d) removing initial samples least affecting the model to alleviate the impact of large increases in the number of new samples on computational complexity. The model was simulated using the automatic identification system tracks of Tianjin port in March 2015. The calculation accuracy and efficiency of this model was verified by comparing the predicted results of the proposed model with the recurrent neural network–long short-term memory, back propagation neural network, and traditional Least-Squares Support Vector Regression models. In sum, the proposed model is highly accurate in online and real-time prediction of a target ship’s trajectory when sailing at sea. In particular, it can sustain high prediction accuracy in the case of smaller data samples. The real-time predicted trajectory can assist the generation of ship collision avoidance decision-making.