基于变换域稀疏压缩感知的艇载稀疏阵列天线雷达实孔径成像

李烈辰; 李道京; 黄平平

doi:10.12000/JR14159

基于变换域稀疏压缩感知的艇载稀疏阵列天线雷达实孔径成像

doi: 10.12000/JR14159

1.
(中国科学院电子学研究所微波成像技术重点实验室北京 100190)
2.
(中国科学院大学北京 100049)
3.
(内蒙古工业大学雷达技术研究所呼和浩特 010051)

基金项目:

国家自然科学基金(61271422, 61201433)

详细信息

作者简介:
李烈辰(1988-),男,浙江杭州人,2010年于中国农业大学获得工学学士学位,现为中国科学院电子学研究所博士研究生,研究方向为基于阵列天线的高分辨率雷达成像技术。E-mail:chrislee365@hotmail.com李道京(1964-),男,陕西西安人,研究员,博士生导师,分别于1986年和1991年在南京理工大学获得工学学士和工学硕士学位,2003年于西北工业大学获得工学博士学位,2003年至2006年在中国科学院电子学研究所做博士后研究,主要研究方向为雷达系统和雷达信号处理。E-mail:lidj@mail.ie.ac.cn黄平平(1978-),男,山东海阳人,博士,副教授,2003年于山东理工大学获得工学学士学位,2007年于内蒙古工业大学获得工学硕士学位,2010年获中国科学院电子学研究所博士学位,现任内蒙古工业大学雷达技术研究所所长。主要研究方向为合成孔径雷达信号处理和微波遥感应用。E-mail:cimhwangpp@163.com

通讯作者:
李烈辰chrislee365@hotmail.com

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出版历程
- 收稿日期: 2014-12-24
- 修回日期: 2015-04-28
- 网络出版日期: 2016-02-28

Airship Sparse Array Antenna Radar Real Aperture Imaging Based on Compressed Sensing and Sparsity in Transform Domain

1.
(Science and Technology on Microwave Imaging Laboratory, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China)
2.
(University of Chinese Academy of Sciences, Beijing 100049, China)
3.
(Radar Research Institute, Inner Mongolia University of Technology, Hohhot 010051, China)

Funds:

The National Natural Science Foundation of China (61271422, 61201433)

摘要

摘要: 该文针对飞艇平台,设计了基于组合巴克码的共形稀疏阵列,并对稀疏阵列的探测性能进行了分析。利用飞艇悬停的特点,可对前后不同时刻脉冲的实孔径成像结果进行干涉处理,去除散射单元的随机初相位,使图像在变换域稀疏。引入压缩感知方法,建立回波与变换域系数的关系,完成对地成像,可获得接近满阵阵列成效的图像质量。仿真数据处理结果验证了该方法的有效性。
- 压缩感知(CS) /
- 变换域稀疏 /
- 共形稀疏阵列 /
- 实孔径成像
Abstract: A conformal sparse array based on combined Barker code is designed for airship platform. The performance of the designed array such as signal-to-noise ratio is analyzed. Using the hovering characteristics of the airship, interferometry operation can be applied on the real aperture imaging results of two pulses, which can eliminate the random backscatter phase and make the image sparse in the transform domain. Building the relationship between echo and transform coefficients, the Compressed Sensing (CS) theory can be introduced to solve the formula and achieving imaging. The image quality of the proposed method can reach the image formed by the full array imaging. The simulation results show the effectiveness of the proposed method.
- Compressed Sensing (CS) /
- Sparsity in the transform domain /
- Conformal sparse array /
- Real aperture imaging

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参考文献(20)

[1]	Barbier C, Delaur B, and Lavie A. Strategic research agenda for high-altitude aircraft and airship remote sensing applications[C]. USE-High Altitude Aircrafts and Airships Workshop, Antwerp, Belgium, 2006: 44-49.
[2]	李道京, 侯颖妮, 滕秀敏, 等. 稀疏阵列天线雷达技术及其应用[M]. 北京: 科学出版社, 2014: 9-40. Li Dao-jing, Hou Ying-ni, Teng Xiu-min, et al.. Sparse A r r a y A n t e n n a R a d a r I m a g i n g T e c h n o l o g y a n d Application[M]. Beijing: Science Press, 2014: 9-40.
[3]	Donoho D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.
[4]	Candes E and Romberg J. Sparsity and incoherence in compressive sampling[J]. Inverse Problems, 2007, 23(3): 969-988.
[5]	滕秀敏, 李道京. 艇载共形稀疏阵列天线雷达成像研究[J]. 电波科学学报, 2012, 27(4): 644-649, 656.
[6]	Teng Xiu-min and Li Dao-jing. Study on airship conformal sparse array radar imaging[J]. Chinese Journal of Radio Sciences, 2012, 27(4): 644-649, 656. Zhu X X and Bamler R. Superresolving SAR tomography for multidimensional imaging of urban areas: compressive sensing-based TomoSAR inversion[J]. Signal Processing Magazine, 2014, 31(4): 51-58.
[7]	吴一戎, 洪文, 张冰尘, 等. 稀疏微波成像研究进展[J]. 雷达学报, 2014, 3(4): 383-395. Wu Yi-rong, Hong Wen, Zhang Bing-chen, et al.. Current developments of sparse microwave imaging[J]. Journal of Radars, 2014, 3(4): 383-395.
[8]	Zhang L, Xing M, Qiu C-W, et al.. Achieving higher resolution ISAR imaging with limited pulses via compressed sampling[J]. IEEE Geoscience and Remote Sensing Letters, 2009, 6(3): 567-571.
[9]	李烈辰, 李道京. 基于压缩感知的连续场景稀疏阵列SAR三维成像[J]. 电子与信息学报, 2014, 36(9): 2166-2172. Li Lie-chen and Li Dao-jing. Sparse array SAR 3D imaging for continuous scene based on compressed sensing[J]. Journal of Electronics Information Technology, 2014, 36(9): 2166-2172.
[10]	杨波. 一种设计组合巴克码脉冲压缩旁瓣抑制滤波器的新方法[J]. 现代雷达, 2001, 23(5): 41-45. Yang Bo. A new method for designing range-sidelobe suppression filter for combined Barker code[J]. Modern Radar, 2001, 23(5): 41-45.
[11]	Li L and Li D. Airship sparse array antenna radar performance analysis[C]. 2013 IEEE International Geoscience Remote Sensing Symposium, Melbourne, Australia, 2013: 628-631.
[12]	Li D, Zhang Q, Li L, et al.. Sparsity analysis of SAR signal and three-dimensional imaging of sparse array SAR[C]. 2013
[13]	IEEE International Geoscience Remote Sensing Symposium, Melbourne, Australia, 2013: 891-894. 张清娟, 李道京. 干涉SAR图像数据压缩[J]. 中国科学院研究生院学报, 2013, 30(3): 380-386. Zhang Qing-juan and Li Dao-jing. InSAR imaging data compression[J]. Journal of Graduate University of Chinese Academy of Sciences, 2013, 30(3): 380-386.
[14]	Li L, Li D, and Pan Z. InSAR signal sparse sampling and processing based on compressed sensing[C]. 10th European Conference on Synthetic Aperture Radar, Berlin, Germany, 2014: 1041-1044.
[15]	Xu G, Xing M D, Xia X G, et al.. Sparse regularization of interferometric phase and amplitude for InSAR image formation based on Bayesian representation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(4): 2123-2136.
[16]	Zeng J, Fang J, and Xu Z. Sparse SAR imaging based on L1/2 regularization[J]. SCIENCE CHINA Information Sciences, 2012, 55(8): 1755-1775.
[17]	Zeng J, Xu Z, Zhang B, et al.. Accelerated L1/2 regularization based SAR imaging via BCR and reduced Newton skills[J]. Signal Processing, 2013, 93(7): 1831-1844.
[18]	辛肖明, 陈琼. m序列优选对及平衡Gold码序列[J]. 北京理工大学学报, 1990, 10(4): 106-113. Xin Xiao-ming and Chen Qiong. Optimum m-sequence pairs and balanced gold group[J]. Journal of Beijing Institute of Technology, 1990, 10(4): 106-113.
[19]	Kim J H, Younis M, and Moreira A. A novel OFDM chirp waveform scheme for use of multiple transmitters in SAR[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 6(3): 568-572.
[20]	Wang W Q. MIMO SAR OFDM chirp waveform diversity design with random matrix modulation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(3): 1615-1625.