结合MD自聚焦算法与回波模拟算子的快速稀疏微波成像误差补偿算法

张柘; 张冰尘; 洪文; 吴一戎

doi:10.12000/JR15055

结合MD自聚焦算法与回波模拟算子的快速稀疏微波成像误差补偿算法

DOI: 10.12000/JR15055

1.
(微波成像技术重点实验室北京 100190)
2.
(中国科学院电子学研究所北京 100190)
3.
(中国科学院大学北京 100190)

基金项目:

国家973项目(2010CB731905)稀疏微波成像的理论、体制和方法研究

详细信息

作者简介:
张柘(1988-),男,中国科学院电子学研究所博士生,研究方向为稀疏微波成像。张冰尘(1973-),男,中国科学院电子学研究所研究员,研究方向为稀疏微波成像。洪文(1968-),女,中国科学院电子学研究所研究员,研究方向为微波成像技术与应用。吴一戎(1964-),男,中国科学院电子学研究所研究员,中国科学院院士,研究方向为微波成像技术与应用。

通讯作者:
张柘pzhgrsrs@gmail.com

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出版历程
- 收稿日期: 2015-05-08
- 修回日期: 2015-06-06
- 网络出版日期: 2016-02-28

Accelerated Sparse Microwave Imaging Phase Error Compensation Algorithm Based on Combination of SAR Raw Data Simulator and Map-drift Autofocus Algorithm

1.
(Science and Technology on Microwave Imaging Laboratory, Beijing 100190, China)
2.
(Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China)
3.
(University of Chinese Academy of Sciences, Beijing 100190, China)

Funds:

The National Basic Research Program (973 Program) of China under grant 2010CB731905 Studies on theory, system, and methodology of Sparse Microwave Imaging

摘要

摘要: 稀疏微波成像是将稀疏信号处理理论引入微波成像中,利用系统的稀疏约束突破传统合成孔径雷达(SAR)成像中系统复杂度的瓶颈,是微波成像的新理论、新体制和新方法。在传统的机载SAR成像中都会面临非理想运动带来的回波相位误差问题,可通过基于回波数据的自聚焦算法加以解决;但在机载稀疏微波成像中,因稀疏微波成像采用稀疏重建算法取代了传统SAR中基于匹配滤波的信号处理方法,传统的基于回波数据的自聚焦算法难以直接应用。现有基于稀疏重建的自聚焦算法主要基于两步迭代方法,收敛速度慢、运算量大。该文以基于回波模拟算子的快速稀疏微波成像算法为基础,将子孔径相关(MD)自聚焦算法引入,与之结合构建了新的MD-回波模拟算子自聚焦算法。该方法继承了基于回波模拟算子算法快速重建的优势,并利用MD自聚焦算法实现了回波2次相位误差的正确补偿,与现有基于两步迭代的稀疏微波成像自聚焦算法相比,收敛速度快,并可以实现较好的自聚焦效果。
- 稀疏微波成像 /
- 合成孔径雷达(SAR) /
- 相位误差 /
- 自聚焦 /
- 子孔径相关 /
- 回波模拟算子
Abstract: Sparse microwave imaging is new concept, theory and methodology of microwave imaging, which introduces the sparse signal processing theory to microwave imaging and combines them together to overcome the paradox of increasing system complexity and imaging performance of current Synthetic Aperture Radar (SAR) systems. Traditional airborne SAR systems are facing a phase error problem in the echo which is caused by the non-ideal motion of the aircraft. This phase error could be compensated by autofocus algorithms. But in the sparse microwave imaging, such autofocus algorithm are no longer valid because traditional signal processing based on matched filtering has been replaced with sparse reconstruction. Current autofocus algorithms under sparse constraints are usually based on a two-step iteration, which convergences slowly and costs plenty of computation. In this paper, we introduce the Map-Drift (MD) autofocus algorithm to the accelerated sparse microwave imaging algorithm based on SAR raw data simulator, and propose the novel MD-SAR raw data simulator autofocus algorithm. This algorithm keeps the advantages of both accelerated imaging algorithm and MD algorithm, including the fast convergence and accurate compensation of two-order phase error in echo. Compared with current algorithms based on two-step iteration, the propose method convergences fast and effectively.
- Sparse microwave imaging /
- Synthetic Aperture Radar (SAR) /
- Phase error /
- Autofocus /
- Map-Drift (MD) /
- SAR raw data simulator

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参考文献(27)

[1]	Zhang B, Hong W, and Wu, Y. Sparse microwave imaging: principles and applications[J]. Science China Information Science (Series F), 2012, 55(8): 1722-1754.
[2]	Hong W, Zhang B, Zhang Z, et al.. Radar imaging with sparse constraint: principle and initial experiment[C]. Proceedings of 10th European Conference on Synthetic Aperture Radar (EUSAR) 2014, Berlin, Germany, 2014: 1-4.
[3]	Fang J, Zhang B, Xu Z, et al.. On selection of the observation model for multilook application of sparse microwave imaging[C]. Proceedings of 10th European Conference on Synthetic Aperture Radar, EUSAR 2014, Berlin, Germany, 2014: 1-4.
[4]	Fang J, Xu Z, Jiang C, et al.. SAR range ambiguity suppression via sparse regularization[C]. 2012 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Mnich, Germany, 2012: 3811-3814.
[5]	吴一戎, 洪文, 张冰尘, 等. 稀疏微波成像研究进展(科普类)[J]. 雷达学报, 2014, 3(4): 383-395. Wu Y, Hong W, Zhang B, et al.. Current developments of sparse microwave imaging[J]. Journal of Radars, 2014, 3(4): 383-395.
[6]	廖明生, 魏恋欢, 汪紫芸, 等. 压缩感知在城区高分辨率SAR层析成像中的应用[J]. 雷达学报, 2015, 4(2): 123-129. Liao M, Wei L, Wang Z, et al.. Compressive sensing in high-resolution 3D SAR tomography of urban scenarios[J]. Journal of Radars, 2015, 4(2): 123-129.
[7]	Brown W M. SAR resolution in the presence of phase errors[J]. IEEE Transactions on Aerospace and Electronic Systems, 1988, 24(6): 808-814.
[8]	张澄波. 综合孔径雷达:原理、分析与应用[M]. 北京: 科学出版社, 1989. Zhang C. Synthetic Aperture Radar: Principle, Analysis and Application[M]. Beijing, Science Press, 1989.
[9]	胡克彬, 张晓玲, 师君, 等. 基于图像强度最优的SAR高精度运动补偿方法[J]. 雷达学报, 2015, 4(1): 60-69. Hu K, Zhang X, Shi J, et al.. A high-precision motion compensation method for SAR based on image intensity optimization[J]. Journal of Radars, 2015, 4(1): 60-69.
[10]	Calloway T M and Donohoe G W. Subaperture autofocus for synthetic aperture radar[J]. IEEE Transactions on Aerospace and Electronic Systems, 1994, 30(2): 617-621.
[11]	李银伟, 陈立福, 韦立登, 等. 基于多普勒域多通道的机载合成孔径雷达自聚焦算法[J]. 电子与信息学报, 2015, 37(4): 969-974. Li Y, Chen L, Wei L, et al.. An autofocus algorithm based on Doppler-domain multichannel for airborne SAR[J]. Journal of Electronics Information Technology, 2015, 37(4): 969-974.
[12]	Eichel P and Jakowatz C Jr. Phase-gradient algorithm as an optimal estimator of the phase derivative[J]. Optics Letters, 1989, DOI: 10.1364/OL.14.001101.
[13]	Carrara W G, Goodman R S, and Majewski R M. Spotlight Synthetic Aperture Radar -Signal Processing Algorithms[M]. Norwood: MA, Artech House, 1995.
[14]	赵曜, 张冰尘, 洪文, 等. 基于RIPless 理论的稀疏微波成像波形分析方法[J]. 雷达学报, 2013, 2(3): 265-270. Zhao Y, Zhang B, Hong W, et al.. RIPless based radar waveform analysis in sparse microwave imaging[J]. Journal of Radars, 2013, 2(3): 265-270.
[15]	蒋成龙, 赵曜, 张柘, 等. 基于相关准则的稀疏微波成像方位向采样优化方法[J]. 电子与信息学报, 2015, 37(3): 580-586. Jiang C, Zhao Y, Zhang Z, et al.. Azimuth sampling optimization scheme for sparse microwave imaging based on mutual coherence criterion[J]. Journal of Electronics and Information Technology, 2015, 37(3): 580-586.
[16]	Xiang Y, Zhang B, and Hong W. Study on the sparse sub-block microwave imaging based on lasso[J]. Journal of Radars, 2013, 2(3): 271-277.
[17]	Onhon N O and Cetin M. A sparsity-driven approach for joint SAR imaging and phase error correction[J]. IEEE Transactions on Image Processing, 2012, 21(4): 2075-2088.
[18]	Ugur S and Ar?kana O. SAR image reconstruction and autofocus by compressed sensing[J]. Digital Signal Processing, 2012, 22(6): 923-932.
[19]	etin M, nhon N O, and Samadi S. Handling phase in sparse reconstruction for SAR: Imaging, autofocusing, and moving targets[C]. European Conference on Synthetic Aperture Radar (EUSAR) 2012, Nuremberg, Germany, 2012: 207-210.
[20]	nhon N O and etin M. SAR moving target imaging using group sparsity[C]. 2013 Proceedings of the 21st European Signal Processing Conference (EUSIPCO), Morocco, 2013: 1-5.
[21]	Giusti E, Tomei S, Bacci A, et al.. Autofocus for CS based ISAR imaging in the presence of gapped data[C]. Workshop on Compressive Sensng Applied to Radar (CoSeRa) 2013, 2013: 1-4.
[22]	Kelly S I, Yaghoobi M, and Davies M E. Auto-focus for compressively sampled SAR[C]. Workshop on Compress Sensng Applied to Radar (CoSeRa) 2012, Bonn, Germany, 2012.
[23]	Ugur S, Ar?kan O, and Gurbuz A C. Autofocused sparse SAR image reconstruction by EMMP algorithm[C]. Workshop on Compressive Sensng Applied to Radar (CoSeRa) 2012, Bonn, Germany, 2012.
[24]	Fang J, Xu Z, Zhang B, et al.. Fast compressed sensing SAR imaging based on approximated observation[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(1): 352-363.
[25]	Jiang C, Zhang B, Fang J, et al.. Efficient lq regularization algorithm with range-azimuth decoupled for SAR imaging[J]. Electronics Letters, 2014, 50(3): 204-205.
[26]	Cands E J and Tao T. Decoding by linear programming[J]. IEEE Transactions on Information Theory, 2005, 51(12): 4203-4215.
[27]	Kasilingam D, Wang J, Lee J, et al.. Focusing of synthetic aperture radar images of moving targets using minimum entropy adaptive filters[C]. IEEE Proceedings of International Geoscience and Remote Sensing Symposium, Honolulu, HI, 2000, 1: 74-76.