雷达辐射源信号分选研究进展

隋金坪; 刘振; 刘丽; 黎湘

doi:10.12000/JR21147

雷达辐射源信号分选研究进展

DOI: 10.12000/JR21147

隋金坪¹,
刘振^2, ,,
刘丽^3, ,,
黎湘²

1.
海军大连舰艇学院作战软件与仿真研究所大连 116016
2.
国防科技大学电子科学学院长沙 410073
3.
国防科技大学系统工程学院长沙 410073

基金项目: 国家自然科学基金优秀青年科学基金(62022091)

详细信息

作者简介:
隋金坪(1990–)，男，博士，2020年12月获国防科技大学信息与通信工程博士学位，2017–2019年曾作为联合培养博士生赴芬兰阿尔托大学计算机科学系从事大数据处理、机器学习等研究工作。现为海军大连舰艇学院作战软件与仿真研究所助理研究员。主要研究方向为雷达目标识别与对抗、智能态势感知与认知

刘　振(1983–)，男，国防科技大学电子科学学院教授，学校领军人才培养对象，国家自然科学基金优秀青年科学基金获得者。研究方向为雷达目标识别与对抗，主要涉及雷达成像与识别、雷达有源对抗、模式识别与机器学习等

刘　丽(1982–)，女，国防科技大学系统工程学院研究员。主要研究方向为计算机视觉与机器学习

黎　湘(1967–)，男，中国科学院院士，国防科技大学教授。研究方向为雷达目标识别，主要涉及雷达目标特性、雷达目标成像与识别、雷达识别对抗等

通讯作者:
刘振 zhen_liu@nudt.edu.cn

刘丽 lilyliu_nudt@163.com

责任主编：普运伟 Corresponding Editor: PU Yunwei
中图分类号: TN911.7
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- 文章访问数: 4560
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- PDF下载量: 1040
- 被引次数: 0
出版历程
- 收稿日期: 2021-10-07
- 修回日期: 2021-12-16
- 网络出版日期: 2022-01-07
- 刊出日期: 2022-06-28

Progress in Radar Emitter Signal Deinterleaving

SUI Jinping¹,
LIU Zhen^{2
, ,},
LIU Li^{3
, ,},
LI Xiang²

1.
Operational Software and Simulation Research Institute, Dalian Naval Academy, Dalian 116016, China
2.
College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China
3.
College of System Engineering, National University of Defense Technology, Changsha 410073, China

Funds: The National Natural Science Foundation of China (62022091)

More Information

Corresponding author: LIU Zhen, zhen_liu@nudt.edu.cn; LIU Li, lilyliu_nudt@163.com

摘要

摘要: 雷达辐射源信号分选是雷达信号侦察的关键技术之一，同时也是战场态势感知的重要环节。该文系统梳理了雷达辐射源信号分选的主流技术，从基于脉间调制特征、基于脉内调制特征、基于机器学习的雷达辐射源信号分选3个角度阐述了目前雷达辐射源信号分选工作的主要研究方向及进展，并重点阐释了基于深度神经网络、数据流聚类等最新分选技术的原理与特点。最后，对现有雷达辐射源信号分选技术的不足进行了总结并对未来趋势进行了预测。
- 雷达辐射源 /
- 脉冲分选 /
- 脉冲流 /
- 机器学习 /
- 深度学习 /
- 数据流聚类
Abstract: Radar emitter signal deinterleaving is a key technology for radar signal reconnaissance and an essential part of battlefield situational awareness. This paper systematically sorts out the mainstream technology of radar emitter signal deinterleaving. It summarizes the main research progress in radar emitter signal deinterleaving from three directions: interpulse modulation characteristics-based, intrapulse modulation characteristics-based, and machine learning-based research. Particularly, this paper focuses on explaining the principle and technical characteristics of the latest deinterleaving technology, such as neural network-based and data stream clustering-based techniques. Finally, the shortcomings of the current radar emitter deinterleaving technology are summarized, and the future trend is predicted.
- Radar emitter /
- Pulse deinterleaving /
- Pulse stream /
- Machine learning /
- Deep learning /
- Data stream clustering

HTML全文

图 1 雷达辐射源分选示意图

Figure 1. Diagram of radar emitter signal deinterleaving

下载: 全尺寸图片幻灯片

图 2 雷达辐射源信号分选发展脉络

Figure 2. The development of radar emitter signal deinterleaving

下载: 全尺寸图片幻灯片

图 3 脉冲描述子各参数的物理意义

Figure 3. The physical meaning of each parameter of PDW

下载: 全尺寸图片幻灯片

图 4 基于数据流聚类的雷达辐射源在线分选处理统一框架

Figure 4. A unified framework for online radar emitter deinterleaving based on data stream clustering

下载: 全尺寸图片幻灯片

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