时序数据图像化：战术意图识别及可移植框架

宋亚飞; 李乐民; 权文; 倪鹏; 王科

doi:10.11959/j.issn.1000-436x.2024154

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时序数据图像化：战术意图识别及可移植框架

学术论文 | 更新时间：2024-09-10

- 时序数据图像化：战术意图识别及可移植框架
- Timing data visualization: tactical intent recognition and portable framework
- 通信学报 2024年45卷第8期页码：149-165
- 作者机构：
  
  1.空军工程大学防空反导学院，陕西西安 710051
  2.复杂航空系统仿真重点实验室，北京 100076
- 作者简介：
  
  [ "宋亚飞（1988- ），男，河南汝州人，博士，空军工程大学教授、博士生导师，主要研究方向为模式识别、智能信息处理、网络空间安全。" ]
  [ "李乐民（1999- ），男，重庆人，空军工程大学硕士生，主要研究方向为智能信息处理、态势感知。" ]
  [ "权文（1988- ），女，陕西蒲城人，博士，空军工程大学讲师，主要研究方向为机器学习及其在空管领航、目标识别等领域的应用。" ]
  [ "倪鹏（1985- ），男，福建福州人，博士，复杂航空系统仿真重点实验室工程师，主要研究方向为作战建模与仿真。" ]
  [ "王科（1999- ），男，重庆人，空军工程大学硕士生，主要研究方向为模式识别、态势感知。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61806219;61703426;61876189);陕西省自然科学基金资助项目(2021JM-226);陕西省高校科协青年人才托举计划基金资助项目(20190108;20220106);陕西省创新能力支撑计划基金资助项目(2020KJXX-065)
- DOI：10.11959/j.issn.1000-436x.2024154
  中图分类号： V219;TP173;V917
- 收稿日期：2024-05-09，
  
  修回日期：2024-07-29，
  
  纸质出版日期：2024-08-25
- 稿件说明：
移动端阅览
宋亚飞,李乐民,权文等.时序数据图像化：战术意图识别及可移植框架[J].通信学报,2024,45(08):149-165.

SONG Yafei,LI Lemin,QUAN Wen,et al.Timing data visualization: tactical intent recognition and portable framework[J].Journal on Communications,2024,45(08):149-165.
宋亚飞,李乐民,权文等.时序数据图像化：战术意图识别及可移植框架[J].通信学报,2024,45(08):149-165. DOI： 10.11959/j.issn.1000-436x.2024154.

SONG Yafei,LI Lemin,QUAN Wen,et al.Timing data visualization: tactical intent recognition and portable framework[J].Journal on Communications,2024,45(08):149-165. DOI： 10.11959/j.issn.1000-436x.2024154.

摘要

通过将时序编码为图像，提出了一种结合曲线滤波技术和EfficientNetV2图像识别网络的鲁棒且可移植的战术意图识别框架。曲线滤波技术可以有效地减少大量时域特征、模型参数和训练时间的冗余，基于此，提出了一种改进的格拉姆角场方法将时序编码为图像，提高了卷积神经网络的特征提取能力。EfficientNetV2网络能够有效地处理意图图像，并成为预训练模型，使得在不同系统之间进行迁移学习成为可能。实验结果表明，所提框架相对于机器学习及深度学习等方法提高了0.99%以上的准确率，具有更好的性能、可扩展性、鲁棒性和可迁移性。

Abstract

By transforming time series into images

a robust and transferable tactical intent recognition framework was proposed

which integrated curve filtering technology and the EfficientNetV2 image recognition network. Curve filtering technology effectively reduced redundancy in numerous time-domain features

model parameters

and training time

an enhanced Gramian angular field (GAF) method was proposed to encode time series into images

enhancing the feature extraction capabilities of convolutional neural networks. The EfficientNetV2 network was adept at processing intent images and could serve as a pre-trained model

facilitating transfer learning across different systems. Experimental results demonstrate that the proposed framework achieves over 0.99% higher accuracy compared to machine learning and deep learning methods

exhibiting superior performance

scalability

robustness

and transferability.

关键词

Keywords

references

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