深度学习在雷达中的研究综述

王俊; 郑彤; 雷鹏; 魏少明

doi:10.12000/JR18040

深度学习在雷达中的研究综述

DOI: 10.12000/JR18040 CSTR: 32380.14.JR18040

北京航空航天大学电子信息工程学院北京 100191

基金项目: 国家自然科学基金（61501011，61671035）

详细信息

作者简介:
王　俊(1972–)，男，教授。现于北京航空航天大学电子信息工程学院从事科研教学工作。1995年于西北工业大学获通信工程专业工学学士学位，1998年、2001年于北京航空航天大学分别获信号与信息处理专业工学硕士学位、信号与信息处理专业工学博士学位。现为中国电子学会高级会员。研究方向为雷达信号处理、FPGA/DSP嵌入式系统、目标识别与跟踪。E-mail: wangj203@buaa.edu.cn

郑　彤(1991–)，女，博士生。分别于2014年及2017年获北方工业大学电子信息工程专业学士、信息与通信工程专业硕士学位。现攻读北京航空航天大学信号与信息处理专业博士学位。主要研究方向为信号处理、模式识别。

雷　鹏(1985–)，男，2006年获北京航空航天大学电子信息工程专业学士学位，2012年获北京航空航天大学信号与信息处理专业博士学位。现任北京航空航天大学电子信息工程学院讲师。主要研究领域为数字信号与图像处理、目标识别

魏少明(1985–)，男，实验师。现于北京航空航天大学电子信息工程学院从事科研与实验教学工作。2007年于北京航空航天大学获通信工程专业工学学士学位，2014年于北京航空航天大学获信号与信息处理专业工学博士学位。现为中国电子学会会员。研究方向为雷达信号处理、多目标跟踪、3维成像

通讯作者:
王俊　 wangj203@buaa.edu.cn

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出版历程
- 收稿日期: 2018-05-22
- 修回日期: 2018-07-18
- 网络出版日期: 2018-08-28

Study on Deep Learning in Radar

School of Electronic and Information Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China

Funds: The National Natural Science Foundation of China (61501011, 61671035)

摘要

摘要: 雷达通过发射天线发射电磁波，经过不同物体反射接收到相应的反射波，对其接收结果进行分析，能得到物体距雷达的位置，径向运动速度等信息，所以对雷达信号的分析具有重要的研究意义。近些年深度学习成为各个领域的研究热点，而在雷达领域同样可通过深度学习算法实现对信号的相应的信息处理。与传统方法相比，深度学习算法具有自动提取深层特征、获取较高准确率等优势。该文具体介绍了近期典型的深度学习算法在雷达信号处理中的应用及研究情况。此外，该文介绍了两个在雷达领域中应用深度学习亟待解决的问题，即过拟合和可解译性。
- 雷达 /
- 深度学习 /
- 信号处理
Abstract: Electromagnetic waves are transmitted by radars and reflected by different objects, and radar signal processing is highly significant as its analyses can lead to the acquisition of important information such as the situation and radial movement speed. Moreover, deep learning has gained much attention in several fields, and it can be utilized to implement radar signal processing. Compared with the traditional methods, deep learning can realize automatic feature extraction and yield highly accurate results; hence, in this paper, the application of deep learning algorithm in radar signal processing is studied. In addition, the study directions in radar signal processing are summarized into overfitting and interpretation. Thus, these two issues are being considered.
- Radar /
- Deep learning /
- Signal processing

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图 1 本文介绍流程

Figure 1. Flow chart of this paper

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图 2 CNN结构示意图

Figure 2. Typical CNN structure

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图 3 AE结构示意图

Figure 3. Typical AE structure

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图 4 DBN结构示意图

Figure 4. Typical DBN structure

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图 5 MSTAR数据示意图

Figure 5. Illustration of MSTAR data

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图 6 4种目标HRRP示意图

Figure 6. HRRPs of four targets

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图 7 两仿真目标时频谱图

Figure 7. Time-frequency map pf two simulation targets

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图 8 4种手势R-D谱图

Figure 8. R-D map of four gestures

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图 9 两种检测网络架构

Figure 9. Two detection structure

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