Vessel trajectory prediction based on recurrent neural network

被引：12

作者：

Hu Y. ^{[1
,2
]}

Xia W. ^{[1
,2
]}

Hu X. ^{[1
,2
]}

Sun H. ^{[1
,2
]}

Wang Y. ^{[1
,2
]}

机构：

[1] School of Management, Hefei University of Technology, Hefei

[2] Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education, Hefei

来源：

Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2020年 / 42卷 / 04期

关键词：

Automatic identification system; Gated recurrent unit; Symmetrized segment-path distance; Trajectory prediction;

D O I：

10.3969/j.issn.1001-506X.2020.04.18

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In maritime search and rescue, customs anti-smuggling and other scenarios, it is often necessary to forecast vessels' trajectory. In order to improve the accuracy and efficiency of the prediction, a method for vessel trajectory prediction based on recurrent neural network is proposed. The method includes data preprocessing and neural network prediction. In data preprocessing, a data preprocessing method based on symmetric segmented-path distance is designed to eliminate the influence of a large number of redundant data and noise. In the prediction of neural network, the model of recurrent neural network with gated recurrent unit as the core is constructed to realize the accurate and efficient prediction of vessels'position information. Comparative experiment is made through a large number of data from the automatic identification system, and experiment results prove that the proposed method is practical and effective. © 2020, Editorial Office of Systems Engineering and Electronics. All right reserved.

引用

页码：871 / 877

页数：6

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