基于样条插值的轻量化AIS船舶轨迹预测

胡建涛; 李天姣; 刘辉; 李舒心; 程徐

doi:10.11993/j.issn.2096-3920.2024-0164

基于样条插值的轻量化AIS船舶轨迹预测

doi: 10.11993/j.issn.2096-3920.2024-0164

胡建涛^1,,
李天姣^2, ,,
刘辉¹,
李舒心¹,
程徐¹

1.
天津理工大学计算机科学与工程学院, 天津, 300382
2.
天津理工大学电气工程与自动化学院, 天津, 300382

基金项目: 国家自然科学基金优秀青年基金(T2422015); 国家自然科学基金青年基金(62306212); 国家自然科学基金重点国际(地区)合作研究项目(2020106004).

详细信息

通讯作者:
李天姣(1994-), 女, 博士, 讲师, 主要研究方向为水下导航及组合导航.

中图分类号: TJ630.33; U675.79
计量
- 文章访问数: 30
- HTML全文浏览量: 9
- PDF下载量: 4
- 被引次数: 0
出版历程
- 收稿日期: 2024-12-11
- 修回日期: 2025-01-24
- 录用日期: 2025-02-12
- 网络出版日期: 2025-03-18

Prediction of AIS Ship Trajectories with a Lightweight Network based on Spline Interpolation

HU Jiantao^1
,,
LI Tianjiao^{2
, ,},
LIU Hui¹,
LI Shuxin¹,
CHENG Xu¹

1.
School of Computer Science and Engineering, Tianjin University of Technology, Tianjin 300382, China
2.
School of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin 300382, China

摘要

摘要: 船舶自动识别系统(AIS)提供着大量的实时船舶航行数据, 已成为海上交通管理、搜救行动和风险评估等诸多领域不可或缺的关键数据来源。其中船舶轨迹预测问题受到广泛关注, 然而, 实现精准的长时间轨迹预测面临两大问题: 一是AIS数据本身的完整性问题; 二是预测模型的效率问题。因此, 如何有效地处理AIS数据缺失以及如何构建轻量且高效的预测模型成为了亟待解决的关键问题。文中提出了一种基于样条插值的轻量化AIS船舶轨迹预测方法, 采用样条插值来填补缺失的AIS数据, 并引入了一种轻量化的线性层结构以降低深度学习模型的复杂度。实验结果表明, 该方法能够有效插值缺失的AIS数据, 显著减少深度学习模型的参数量和计算量, 进而提升对船舶轨迹的预测精度。
- AIS数据 /
- 样条插值 /
- 模型轻量化 /
- 轨迹预测
Abstract: Automatic Identification System (AIS) provides a large amount of real-time ship navigation data, which has become an indispensable key data source in many fields such as maritime traffic management, search and rescue operations and risk assessment. Among them, ship trajectory prediction has received wide attention, however, realizing accurate long-time trajectory prediction faces two major problems: one is the integrity of AIS data itself, and the other is the efficiency of prediction models. Therefore, how to effectively deal with the missing AIS data and how to construct a lightweight and efficient prediction model have become the key problems to be solved. In this paper, a lightweight network AIS ship trajectory prediction method based on spline interpolation is proposed. Spline interpolation is used to fill in the missing AIS data, and a lightweight linear layer structure is introduced to reduce the complexity of the deep learning model. The experimental results show that the method can effectively interpolate the missing AIS data, significantly reduce the number of parameters and computation of the deep learning model, and then improve the prediction accuracy of ship trajectory.
- AIS data /
- spline interpolation /
- model lightweight /
- trajectory prediction

HTML全文

图 1 基于样条插值的轻量化AIS船舶轨迹预测

Figure 1. Lightweight AIS ship trajectory prediction based on spline interpolation

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图 2 基于Low-Rank Linear层

Figure 2. Based on Low-Rank Linear layer

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图 3 30%缺失率下的AIS数据插值

Figure 3. Interpolation of AIS data at 30% missing rate

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图 4 trAISformer模型结构图以及参数

Figure 4. trAISformer model structure diagram and parameters

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图 5 trAISformer模型轻量化前后参数量和计算量对比

Figure 5. Comparison of parameters and calculation of trAISformer model before and after lightweight

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图 6 30%缺失率下的AIS轨迹预测效果对比,

Figure 6. Comparison of AIS trajectory prediction effectiveness at 30% missing rate

下载: 全尺寸图片幻灯片

表 1 不同AIS缺失率下各类插值方法的平均绝对误差和均方根误差结果

Table 1. Mean absolute error and root mean square error results of various interpolation methods under different AIS deletion rates

误差评价标准	数据缺失率/%	样条插值	KNN	VAE
平均绝对误差	10	124.31	1 217.81	5 929.95
	20	144.89	1 443.74	3 574.67
	30	181.10	1 728.32	2 266.12
均方根误差	10	220.30	1 435.59	104 563.78
	20	256.83	1 443.74	135 865.43
	30	337.21	2 198.57	4 140.36

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表 2 模型轻量化前后性能对比

Table 2. Comparison of model performance before and after lightweighting

预测模型	1 h/km	2 h/km	3 h/km	训练时间/s	评估时间/s
trAISformer	0.9 147	1.7 800	2.9 592	882.4 580	102.7 633
改进的 trAISformer	0.7 875	1.5 427	2.6 187	648.2 574	91.6 179

下载: 导出CSV

表 3 不同AIS缺失率下各插值方法的轨迹预测误差对比

Table 3. Comparison of trajectory prediction errors of each interpolation method under different AIS deletion rates

数据缺失率/%	预测模型	1 h/km	2 h/km	3 h/km
10	trAISformer	1.4 982	2.6 222	3.8 692
10	改进的trAISformer	0.7 875	1.5 427	2.6 187
20	trAISformer	1.9 053	3.2 644	5.1 159
20	改进的trAISformer	0.8 292	1.5 868	2.7 027
30	trAISformer	2.6 061	4.5 061	6.8 471
30	改进的trAISformer	0.8 280	1.6 348	2.6 667

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表 4 基于消融方法评估10%数据缺失率下船舶轨迹预测模型的预测误差

Table 4. An ablation-based approach to evaluate the prediction error of ship trajectory prediction models with 10% missing data rate

数据缺失率/%	预测模型	1 h/km	2 h/km	3 h/km
10	缺乏样条插值以及轻量化方法	1.4 982	2.6 222	3.8 692
	缺乏样条插值	1.3 594	2.3 610	3.5 950
	缺乏轻量化方法	0.8 550	1.6 814	2.7 163
	改进的trAISformer	0.7 875	1.5 427	2.6 187

下载: 导出CSV

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