M-STCP: an online ship trajectory cleaning and prediction algorithm using matrix neural networks

Frontiers in Marine Science

Marine Science

Meng Sun¹  Xiaodong Mao¹  Shuang Wang¹  Shuai Guo²  Huanqun Xue²  Chao Liu³ 

[1] College of Information and Control Engineering, Qingdao University of Technology, Qingdao, China;College of Science, Qingdao University of Technology, Qingdao, China;School of Computer Science and Technology, Ocean University of China, Qingdao, China;

关键词: trajectory prediction;    marine traffic safety;    Global Positioning System;    matrix neural network;    anomaly point removal;   

DOI  :  10.3389/fmars.2023.1199238

 received in 2023-04-03, accepted in 2023-07-31,  发布年份 2023

来源: Frontiers

【 摘 要 】

Accurate prediction of ship trajectories is crucial to guarantee the safety of maritime navigation. In this paper, a matrix neural network-based online ship track cleaning and prediction algorithm called M-STCP is suggested to forecast ship tracks. Firstly, the GPS-provided historical ship trajectory data is cleaned, and the data cleaning process is finished using the anomaly point algorithm. Secondly, the trajectory is input into the matrix neural network for training and prediction, and the algorithm is improved by using Kalman filtering, which reduces the influence of noise on the prediction results and improves the prediction accuracy. In the end, the effectiveness of the method is verified using real GPS trajectory data, and compared with the GRU model and long-short-term memory networks. The M-STCP method can improve the prediction accuracy of ship trajectory to 89.44%, which is 5.17% higher than LSTM and 1.82% higher than GRU, effectively improving the prediction accuracy and time efficiency.

【 授权许可】

Unknown   
Copyright © 2023 Guo, Sun, Xue, Mao, Wang and Liu