参数化稀疏表征在雷达探测中的应用

李刚; 夏香根

doi:10.12000/JR15126

参数化稀疏表征在雷达探测中的应用

DOI: 10.12000/JR15126

李刚^,,
夏香根

1.
(清华大学电子工程系北京 100084)
2.
(特拉华大学电子与计算机工程系纽瓦克 DE 19716)

基金项目:

国家自然科学基金(61422110, 41271011),万人计划青年拔尖人才支持项目,清华大学自主科研项目,清华信息科学与技术国家实验室(筹)拔尖人才支持计划项目

详细信息

作者简介:
李刚(1979-),男,2002年和2007年于清华大学电子系分别获得学士、博士学位,现为清华大学电子系研究员、博士生导师,研究方向包括雷达成像、时频分析、稀疏信号处理、分布式信号处理等。E-mail:gangli@tsinghua.edu.cn夏香根(1963-),男,现为美国特拉华大学教授、博士生导师,研究方向包括空时编码、MIMO和OFDM系统、雷达成像等。E-mail:xxia@ee.udel.edu

通讯作者:
李刚gangli@tsinghua.edu.cn

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- 被引次数: 18
出版历程
- 收稿日期: 2015-12-14
- 修回日期: 2016-01-06
- 网络出版日期: 2016-02-28

Parametric Sparse Representation and Its Applications to Radar Sensing

Li Gang^,,
Xia Xiang-Gen

1.
(Department of Electronic Engineering, Tsinghua University, Beijing 100084, China)
2.
(Department of Electrical & Computer Engineering, University of Delaware, Newark, DE 19716, USA)

Funds:

The National Natural Science Foundation of China (61422110, 41271011), The National Ten Thousand Talent Program-Young Top-Notch Talent Program, The Tsinghua University Initiative Scientific Research Program, The Tsinghua National Laboratory for Information Science and Technology (TNList) Program

摘要

摘要: 稀疏信号处理已经在雷达目标探测领域得到应用,并获得了优于传统方法的探测性能。然而,雷达目标探测过程中往往存在目标运动、雷达轨迹误差等未知因素,这导致预先设计的字典矩阵无法实现雷达信号的最优稀疏表征。该文将介绍字典学习的一个分支参数化稀疏表征,该方法通过构建参数化的字典矩阵,实现了对雷达探测过程中未知参数的动态学习和雷达信号的最优稀疏表征。该文还将介绍参数化稀疏表征在逆合成孔径雷达成像、合成孔径雷达自聚焦、基于微多普勒的目标识别等若干雷达探测问题中的应用。
- 稀疏信号处理 /
- 雷达探测 /
- 字典学习
Abstract: Sparse signal processing has been utilized to the area of radar sensing. Due to the presence of unknown factors such as the motion of the targets of interest and the error of the radar trajectory, a predesigned dictionary cannot provide the optimally spare representation of the actual radar signals. This paper will introduce a method called parametric sparse representation, which is a special case of dictionary learning and can dynamically learn the unknown factors during the radar sensing and achieve the optimally sparse representation of radar signals. This paper will also introduce the applications of parametric sparse representation to Inverse Synthetic Aperture Radar imaging (ISAR) imaging, Synthetic Aperture Radar imaging (SAR) autofocusing and target recognition based on micro-Doppler effect.
- Sparse signal processing /
- Radar sensing /
- Dictionary learning

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参考文献(31)

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