Timing data visualization: tactical intent recognition and portable framework

SONG Yafei; LI Lemin; QUAN Wen; NI Peng; WANG Ke

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

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Timing data visualization: tactical intent recognition and portable framework

Papers | 更新时间：2024-09-10

- Timing data visualization: tactical intent recognition and portable framework
- Journal on Communications Vol. 45, Issue 8, Pages: 149-165(2024)
- 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：
- 基金信息：
  
  The National Natural Science Foundation of China(61806219;61703426;61876189);The National Science Foundation of Shaanxi Province(2021JM-226);The Young Talen Fund of University and Association for Science and Technology in Shaanxi(20190108;20220106);The Innovation Capability Support Plan of Shaanxi(2020KJXX-065)
- DOI：10.11959/j.issn.1000-436x.2024154
  CLC： V219;TP173;V917
- Received：09 May 2024，
  
  Revised：2024-07-29，
  
  Published：25 August 2024
- 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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