基于线性Bregman迭代类的多量测向量ISAR成像算法研究

陈文峰; 李少东; 杨军; 马晓岩

doi:10.12000/JR16057

基于线性Bregman迭代类的多量测向量ISAR成像算法研究

DOI: 10.12000/JR16057

(空军预警学院武汉 430019)

基金项目:

国家部委基金

详细信息

作者简介:
陈文峰(1989–),男,新疆巩留人,2014年获空军预警学院硕士学位,现为空军预警学院博士研究生,主要研究方向为压缩感知、逆合成孔径雷达成像。E-mail:chenwf925@163.com;李少东(1987–),男,河北保定人,2012年获空军预警学院硕士学位,现为空军预警学院博士研究生,主要研究方向为压缩感知、逆合成孔径雷达成像。E-mail:liying198798@126.com;杨军(1973–),男,云南大理人,2003年获空军工程大学博士学位,现为空军预警学院副教授,主要研究方向为压缩感知、雷达成像、雷达系统。E-mail:yangjem@126.com;马晓岩(1961–),男,湖北赤壁人,2002年获清华大学博士学位,现为空军预警学院教授,主要研究方向为雷达成像、雷达系统、目标检测。E-mail:mxyldxy@sina.com

通讯作者:
陈文峰chenwf925@163.com

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出版历程
- 收稿日期: 2016-03-15
- 修回日期: 2016-06-14
- 网络出版日期: 2016-08-28

Multiple Measurement Vectors ISAR Imaging Algorithm Based on a Class of Linearized Bregman Iteration

(Air Force Early Warning Academy, Wuhan 430019, China)

Funds:

The National Ministries Foundation

摘要

摘要: 为实现目标回波数据稀疏时的快速稳健ISAR成像，该文在构建多量测向量ISAR回波模型的基础上，利用压缩感知(Compressive Sensing, CS)中的线性Bregman迭代理论，研究了基于线性Bregman迭代类的多量测向量快速ISAR成像算法。该类成像算法共包括4种算法，首先给出此类算法的整体迭代构架、应用条件以及4种方法之间的联系；其次对此类算法的重构性能、收敛性、抗噪性以及正则化参数选择等方面进行全面的比较分析；最后基于实测数据进行ISAR成像，实验结果表明，与传统单量测向量ISAR成像算法相比，该文算法在低信噪比条件下可在更短的成像时间内获得更高的成像质量。
- 压缩感知 /
- 逆合成孔径雷达 /
- 多量测向量模型 /
- 线性Bregman迭代
Abstract: This study aims to enable steady and speedy acquisition of Inverse Synthetic Aperture Radar (ISAR) images using sparse echo data. To this end, a Multiple Measurement Vectors (MMV) ISAR echo model is studied. This model is then combined with the Compressive Sensing (CS) theory to realize a class of MMV fast ISAR imaging algorithms based on the Linearized Bregman Iteration (LBI). The algorithms involve four methods, and the iterative framework, application conditions, and relationship between the four methods are given. The reconstructed performance of the methods, convergence, anti-noise, and selection of regularization parameters are then compared and analyzed comprehensively. Finally, the experimental results are compared with the traditional Single Measurement Vector (SMV) ISAR imaging algorithm; this comparison shows that the proposed algorithm delivers an improved imaging quality with a low Signal-to-Noise Ratio (SNR).
- Compressive Sensing (CS) /
- Inverse Synthetic Aperture Radar (ISAR) /
- Multiple Measurement Vectors (MMV) model /
- Linearized Bregman Iteration (LBI)

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参考文献(28)

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