基于循环神经网络的船舶航迹预测

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

Abstract

Abstract:

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.

Key words: trajectory prediction, automatic identification system, symmetrized segment-path distance, gated recurrent unit

CLC Number:

TP183

Yuke HU, Wei XIA, Xiaoxuan HU, Haiquan SUN, Yunhui WANG. Vessel trajectory prediction based on recurrent neural network[J]. Systems Engineering and Electronics, 2020, 42(4): 871-877.

Figures/Tables 13

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References 28

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处理器	Intel(R) Core(TM) i5-7500 3.40 GHz
内存	8.00 GB
系统类型	Windows 10操作系统(64位)
程序设计语言	Python 3.6.3
编程环境	Visual Studio Code
模型框架构建	Python的Tensorflow和Keras实现

模型	模型参数	数据组	耗时/s	预测MSE
SSPD 和 GRU	神经元数量=100 样本训练次数=2 批处理数量=32	数据1	102.41	0.001 76
		数据2	110.35	0.004 05
		数据3	118.64	0.006 13
		数据4	103.02	0.003 86
		数据5	121.63	0.006 12
		数据6	107.87	0.002 21
		数据7	104.67	0.001 35
		数据8	123.65	0.005 96
		数据9	110.83	0.002 61
		数据10	107.24	0.003 38

样本训练次数/次	计算耗时/s	MSE
1	53.069	0.004 321
2	103.808	0.003 585
3	154.889	0.002 472
4	204.947	0.002 069
5	259.472	0.001 662
6	314.956	0.001 566
7	366.846	0.001 364
8	420.121	0.001 399
9	478.946	0.001 249
10	524.858	0.001 555

模型	度量指标	批处理数量/个
模型	度量指标	16	24	32
GRU	误差(MSE)	0.002 768	0.002 728	0.003 585
GRU	计算耗时/s	142.932	128.941	103.808

模型	度量指标	神经元数量/个
模型	度量指标	40	60	80	100	120	140	160
GRU	MSE	0.004 489	0.004 239	0.003 885	0.003 585	0.003 871	0.003 494	0.004 841
GRU	计算耗时/s	79.350	94.883	113.567	147.835	169.937	208.444	272.471

Vessel trajectory prediction based on recurrent neural network

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