一种新的基于Omega-K算法的稀疏场景压缩感知SAR成像方法

胡静秋; 刘发林; 周崇彬; 李博; 王东进

doi:10.12000/JR16027

CS-SAR Imaging Method Based on Inverse Omega-K Algorithm

DOI: 10.12000/JR16027

Hu Jingqiu^①,②,
Liu Falin^{①,②
, ,},
Zhou Chongbin^①,②,
Li Bo^①,②,
Wang Dongjin^①,②

①.
Department of EEIS, University of Science and Technology of China, Hefei 230027, China
②.
Key Laboratory of Electromagnetic Space Information, Chinese Academy of Sciences, Hefei 230027, China

Funds:

The National Natural Science Foundation of China 61431016

More Information

Author Bio:
Hu Jingqiu (1992-) was born in Anhui, China. She is currently a master student in University of Science and Technology of China. She received her Bachelor degree in electronic engineering and information science from University of Science and Technology of China. Her current research interests include SAR imaging, compressed sensing, and sparse signal reconstruction

Liu Falin (1963-) was born in Xingtai, Hebei. He received the B.E. degree from Tsinghua University, Beijing, China, in 1985, and the M.E. and Ph.D. degrees both from University of Science and Technology of China (USTC), Hefei, China, in 1988 and 2004, respectively. Since 1988, he has been with the Department of Electronic Engineering and Information Science, USTC, where he is now a professor. His research interests include mm-wave devices, computational electromagnetics, microwave communications, and radar imaging. E-mail:liufl@ustc.edu.cn

Zhou Chongbin (1988-) was born in Luoyang, Henan Province, China. He received the B.Eng. degree from University of Science and Technology of China (USTC), Hefei, China, in 2011. He is currently working toward the Ph.D. degree at USTC. His research interests include compressive sensing, signal processing, and radar imaging

Li Bo (1991-) was born in Baoji, Shaanxi Province, China. He received the Bachelor degree from Xidian University, Xi'an, China, in 2013. He is currently working toward the Ph.D. degree at USTC. His research interests include compressive sensing, signal processing, and radar imaging

Wang Dongjin (1955-) was born in Huainan, Anhui Province. He received the Bachelor's degree from University of Science and Technology of China (USTC), Hefei, China, in 1982, and the M.E. degree from Nanjing Institute of Electronic Technology, Nanjing, China, in 1985. He has been a full professor since 1998. Prof. Wang had been the vice president of USTC since 2003 and is now the director of the Key Laboratory of Electromagnetic Space Information, Chinese Academy of Sciences. His research interests include electromagnetic theory, mm-wave communications and radar systems, and applications

Corresponding author: Liu Falin. E-mail:liufl@ustc.edu.cn

摘要

Abstract: Compressed Sensing (CS) has been proved to be effective in Synthetic Aperture Radar (SAR) imaging. Previous CS-SAR imaging algorithms are very time consuming, especially for producing high-resolution images. In this study, we propose a new CS-SAR imaging method based on the well-known omega-K algorithm, which is precise and convenient to use in SAR imaging. First, we derive an inverse omega-K algorithm to directly obtain echoes without any convolution between the transmitted signal and scene. Then, we formulate the SAR imaging problem into a sparse regularization problem and solve it using an iterative thresholding algorithm. With our derived inverse omega-K algorithm, we can save significant amounts of computation time and computer memory usage. Simulation results show that the proposed method can effectively recover SAR images with much less data than that required by the Nyquist rate.
- Synthetic Aperture Radar (SAR) /
- Compressed Sensing (CS) /
- Omega-K Algorithm (OKA) /
- Iterative thresholding algorithm
摘要: 很多文献已经证明压缩感知应用在SAR成像中的有效性.现有的CS-SAR成像算法非常耗时, 尤其是对于高分辨率的图像来说更甚.该文针对稀疏场景提出了一种基于omega-K算法, 精确且高效的CS-SAR成像算法——CS-OKA算法.我们首先推导出了omega-K算法的逆算子, 可不通过发射信号和场景的卷积来直接得到回波信号.在此基础上我们将SAR成像问题建立为一个稀疏优化问题, 并用迭代阈值的方法来求解.仿真结果表明, 当场景稀疏时该文的方法可以在远低于Nyquist采样率的前提下有效地恢复出原始场景, 并且时耗和存储量都显著降低.
- 合成孔径雷达 /
- 压缩感知 /
- Omega-K算法 /
- 迭代阈值算法

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Figure 1. Reconstruction results with full sampled data but different SNRs. The left column is recovered by omega-K algorithm, and the right column is recovered by CS-OKA. From top to bottom, SNRs are 20, 10, and 5 dB, respectively

下载: 全尺寸图片幻灯片

Figure 2. Contours of magnitude of lower left target in Fig. 1. The left column is recovered by omega-K algorithm, and the right column is recovered by CS-OKA. From top to bottom, SNRs are 20, 10, and 5 dB, respectively

下载: 全尺寸图片幻灯片

Figure 3. Reconstruction results with 50% samples in both range and azimuth. From left to right, SNRs are 20, 10, and 5 dB, respectively

下载: 全尺寸图片幻灯片

Figure 4. Reconstruction results with 10% samples in both range and azimuth. From left to right, SNRs are 20, 10, and 5 dB, respectively

下载: 全尺寸图片幻灯片

Figure 5. Reconstruction results of two close targets

下载: 全尺寸图片幻灯片

Algorithm 1: Iterative thresholding algorithm for proposed CS-SAR imaging
Input: SAR raw echoes S_S,
omega-K algorithm M and inverse omega-K algorithm T,
sampling operator Θ_τ and Θ_η
Initial: G⁽⁰⁾, λ, μ, and max iteration I_max
1: for i = 1 to I_max do
2: residue: ${{\mathit{\boldsymbol{R}}}^{\left( i-1 \right)}}={{\mathit{\boldsymbol{S}}}_{S}}-{\mathit{\Theta }_\tau }T\left( {{\mathit{\boldsymbol{G}}}^{\left( i-1 \right)}} \right){{\mathit{\Theta }}_{\eta }}$
3: omega-K on residue: $\Delta {{\mathit{\boldsymbol{G}}}^{\left( i-1 \right)}}=M\left( \mathit{\Theta } _{\tau }^{\rm{T}}{{\mathit{\boldsymbol{R}}}^{\left( i-1 \right)}}\mathit{\Theta } _{\eta }^{\rm{T}} \right)$
4: Thresholding: ${{\mathit{\boldsymbol{G}}}^{\left( i \right)}}={{E}_{1, \lambda \mu }}\left( {{\mathit{\boldsymbol{G}}}^{\left( i-1 \right)}}+\mu \Delta {{\mathit{\boldsymbol{G}}}^{\left( i-1 \right)}} \right)$
5: end for
Output: The recovery image G^*=G⁽ⁱ⁾

下载: 导出CSV

Table 1. Parameters used in the simulation

Parameter	Value
Pulse duration (μs)	1.33
Bandwidth in range (MHz)	150
Carrier frequency (MHz)	600
Sampling rate (MHz)	225
Slant range of scene center (m)	1200
Length of synthetic aperture (m)	300
Pulse repeat frequency (Hz)	150 000 000
Radar velocity in azimuth (m/s)	150

下载: 导出CSV

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