Affiliation:
1. Navigation College, Jimei University, Xiamen 361021, China
Abstract
This study proposes a solution to the problem of inaccurate and time-consuming ship trajectory prediction caused by frequent ship maneuvering in complex waterways. The proposed solution is a ship trajectory prediction model that uses a difference long short-term memory neural network (D-LSTM). To improve prediction performance and reduce time dependence, the model combines the other variables of dynamic time features in the ship’s Automatic Identification System (AIS) data with nonlinear elements in the sequence data. The effectiveness of this method is demonstrated by comparing its accuracy to other commonly used time series modeling techniques. The results show that the proposed model significantly reduces training time and improves prediction accuracy.
Funder
National Natural Science Foundation of China
Key Projects of National Key R & D Program
Natural Science Project of Fujian Province
Fuzhou-Xiamen-Quanzhou Independent Innovation Region Cooperated Special Foundation
Subject
Ocean Engineering,Water Science and Technology,Civil and Structural Engineering
Reference24 articles.
1. Research on ship positioning navigation and track prediction based on big data technology;Fan;Ship Sci. Technol.,2018
2. Research on estimation of AIS vessel trajectory data based on Kalman filter algorithm;Xu;Mod. Electron. Tech.,2014
3. Vessel trajectory prediction algorithm based on polynomial fitting kalman filtering;Jiang;J. Signal Process.,2019
4. Chellappan, S., Cheng, W., and Li, W. (2018). Wireless Algorithms, Systems, and Applications, Springer International Publishing.
5. Ship trajectory uncertainty prediction based on a gaussian process model;Rong;Ocean. Eng.,2019