基于LBI的二维复稀疏信号重建算法及应用研究

陈文峰; 李少东; 杨军

doi:10.16383/j.aas.2016.c140897

基于LBI的二维复稀疏信号重建算法及应用研究

doi: 10.16383/j.aas.2016.c140897 cstr: 32138.14.j.aas.2016.c140897

空军预警学院武汉 430019

基金项目:

国家自然科学基金 61179014

详细信息

作者简介:
李少东, 空军预警学院博士研究生.2012年获得空军预警学院硕士学位. 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, 空军预警学院副教授.2003年获得空军工程大学博士学位. 主要研究方向为雷达系统, 雷达成像, 压缩感知.E-mail:yangjem@126.com

通讯作者:
陈文峰, 空军预警学院博士研究生.2014年获得空军预警学院硕士学位. 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

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出版历程
- 收稿日期: 2014-12-25
- 录用日期: 2015-12-07
- 刊出日期: 2016-04-01

2D Complex Sparse Reconstruction Algorithm with LBI and Its Application

Air Force Early Warning Academy, Wuhan 430019

Funds:

National Natural Science Foundation of China 61179014

More Information

Author Bio:
Ph. D. candidate at the Air Force Early Warning Academy. He received his master degree from Air Force Early Warning Academy in 2012. His research interest covers compressed sensing and inverse synthetic aperture radar imaging.

Associate professor at the Air Force Early Warning Academy. He received his Ph. D. degree from Air Force Engineering University in 2003. His research interest covers radar system, radar imaging, and compressed sensing.

Corresponding author: CHEN Wen-Feng Ph. D. candi- date at the Air Force Early Warning Academy. He received his master de- gree from Air Force Early Warning Academy in 2014. His research interest covers compressed sensing and inverse syn- thetic aperture radar imaging. Corresponding author of this paper.

摘要

摘要: 针对二维复稀疏信号重建时存在存储空间和计算复杂度增加的问题, 本文提出了一种快速并行重建二维复稀疏信号的并行线性Bregman迭代(Parallel fast linearized Bregman iteration, PFLBI)算法. 首先, 构建了二维复稀疏信号的结构模型以及PFLBI算法基本迭代格式; 其次, 通过变步长方式提高迭代收敛速度, 而每次迭代的步长则是通过估计中间变量的积累量突破收缩阈值需要的积累步数得到的; 再次, 对算法的性能指标进行了分析; 最后, 将该算法应用于逆合成孔径雷达(Inverse synthetic aperture radar, ISAR)成像. 实验结果表明该算法在重建性能和速度上具有优势.
- 稀疏重建 /
- 二维信号处理 /
- 线性Bregman迭代 /
- ISAR 成像
Abstract: A parallel fast linearized Bregman iteration (PFLBI) algorithm is proposed to solve the problem of large storage and complex computation in the reconstruction of 2D sparse signal. The PFLBI algorithm can efficiently reconstruct 2D sparse signal in a parallel way. Firstly, the matrix form of linearized Bregman iteration (LBI) is constructed. Secondly, the convergence speed is improved by estimating the number of the steps for intermediate variables to cross the shrinkage threshold. Thirdly, the performance of the proposed algorithm is analyzed. Finally, PFLBI is applied to inverse synthetic aperture radar (ISAR) imaging. Experimental results show that the proposed algorithm can improve the performance and the speed of reconstruction.
- Sparse reconstruction /
- 2D signal processing /
- linearized Bregman iteration (LBI) /
- inverse synthetic aperture radar (ISAR) imaging

HTML全文

图 1 任意稀疏结构二维复稀疏信号示意图

Fig. 1 The illustration of the 2D complex sparse signal

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图 2 本文算法重建结果

Fig. 2 Reconstruction results by the proposed algorithm

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图 3 算法收敛性验证

Fig. 3 算法收敛性验证

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图 4 相对重建误差与信噪比的关系

Fig. 4 Relationship between relative reconstruction error and SNR

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图 5 不同算法运算时间比较

Fig. 5 Comparison of CPU time of di®erent algorithms

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图 6 目标模型及脉压后结果

Fig. 6 目标模型及脉压后结果

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图 7 不同信噪比仿真数据成像结果

Fig. 7 Images under di®erent SNR by simulation data

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图 8 实测数据脉压后不同信噪比回波结果

Fig. 8 实测数据脉压后不同信噪比回波结果

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图 9 实测数据脉压后不同信噪比回波结果

Fig. 9 Images under di®erent SNR by real data

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