基于压缩感知的合成孔径雷达超分辨成像复数据处理方法研究
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摘要
合成孔径雷达是一种全天时、全天候、大面积的高分辨率成像雷达。伴随着它在军事、国民经济方面的广泛应用,提高其成像分辨率的研究受到越来越大的重视,不同领域的学者分别从软件和硬件两个方向进行了卓有成效的尝试。正则化方法是通过数据处理提高合成孔径雷达分辨率技术的一种,但是正则化模型的最优化求解算法通常要求解在实数据范围内,而对于SAR的复数回波数据无能为力,本文结合课题组在信号稀疏分解与重建领域的研究成果,给出了一种解决这个问题的方法,是在课题组原有研究基础上的进一步延伸。
本文的主要工作包括：
首先,依托课题组在信号的稀疏表示领域的深入研究成果,将压缩感知的思想应用于正则化模型,提出了成像算子的概念,分别就一维距离向、方位向和二维系统精确建模,将SAR成像问题转化为稀疏信号重构问题。
其次,依据土耳其学者Cetin在09年提出的交替迭代的思想,将回波重建过程分为相位和幅值两部分交替处理,实现了合成孔径雷达正则化成像模型中的复数据最优化求解算法。
本文立足于实验,所有结论均通过大量的实验得以证明,为了方便实现数据仿真,专门在matlab环境下实现了可视化的、可即时调整场景参数的应用软件。
Synthetic aperture radar is a all-day, all-weather, large-area and high resolution imaging radar. With its military and national economic aspects of the widespread application, research of improve its imaging resolution ratio become more and more high attention, different scholars respectively from both software and hardware conducted fruitful attempt. Regularization method is one of technical to improve synthetic aperture radar resolution through data processing, but regularization model optimization algorithm usually require a solution in a real data range, while for the plural of SAR echo data is powerless, combining with our achievements in field of signal sparse decomposition and reconstruction, this paper presents a way to solve this problem, it is further extensions on the basis of our original research.
In this paper the main innovation points include:
First, relying on our research team of in-depth research achievements in the field of signal image sparse representation, will compressed sensing applied to regularization model, and then puts forward the concept of imaging operator, precise modeling for one-dimensional distance, orientation and 2-d system respectively, will SAR imagery problem into a signal reconfiguration problem.
Secondly, according to the Turkish scholars Cetin proposed alternating iterative thoughts in 2009, will echo reconstruction divided into two parts of phase and amplitude, which can be alternant processed, realized the complex data optimization algorithm of the synthetic aperture radar regular model.
This paper based on the experiment, all conclusions have been proved through a lot of experiments, in order to facilitate realize data simulation, specialized implement the visualization, real-time adjust scene parameters application software in matlab environment.
本文的主要工作包括：
首先,依托课题组在信号的稀疏表示领域的深入研究成果,将压缩感知的思想应用于正则化模型,提出了成像算子的概念,分别就一维距离向、方位向和二维系统精确建模,将SAR成像问题转化为稀疏信号重构问题。
其次,依据土耳其学者Cetin在09年提出的交替迭代的思想,将回波重建过程分为相位和幅值两部分交替处理,实现了合成孔径雷达正则化成像模型中的复数据最优化求解算法。
本文立足于实验,所有结论均通过大量的实验得以证明,为了方便实现数据仿真,专门在matlab环境下实现了可视化的、可即时调整场景参数的应用软件。
Synthetic aperture radar is a all-day, all-weather, large-area and high resolution imaging radar. With its military and national economic aspects of the widespread application, research of improve its imaging resolution ratio become more and more high attention, different scholars respectively from both software and hardware conducted fruitful attempt. Regularization method is one of technical to improve synthetic aperture radar resolution through data processing, but regularization model optimization algorithm usually require a solution in a real data range, while for the plural of SAR echo data is powerless, combining with our achievements in field of signal sparse decomposition and reconstruction, this paper presents a way to solve this problem, it is further extensions on the basis of our original research.
In this paper the main innovation points include:
First, relying on our research team of in-depth research achievements in the field of signal image sparse representation, will compressed sensing applied to regularization model, and then puts forward the concept of imaging operator, precise modeling for one-dimensional distance, orientation and 2-d system respectively, will SAR imagery problem into a signal reconfiguration problem.
Secondly, according to the Turkish scholars Cetin proposed alternating iterative thoughts in 2009, will echo reconstruction divided into two parts of phase and amplitude, which can be alternant processed, realized the complex data optimization algorithm of the synthetic aperture radar regular model.
This paper based on the experiment, all conclusions have been proved through a lot of experiments, in order to facilitate realize data simulation, specialized implement the visualization, real-time adjust scene parameters application software in matlab environment.
引文
[1]邓湘金,王磊,彭海良,吴一戎.合成孔径雷达的发展历史及趋势[J].华北工学院测试技术学报,2000,14(2).
[2]袁小金.合成孔径雷达图像提高分辨率技术研究[D].上海交通大学硕士论文,2007.
[3]J B Boisvert, T J Pultz, R J Brown, B Brisco. Potential of Synthetic Aperture Radar for Large-Scale Soil Moisture Monitoring:A Review. Canadian Journal of Remote Sensing. 1995,22(1):2-13.
[4]Y Grevier,T J Pultz, T I Lukowski, T Toutin. Temporal Analysis of ERS-1 SAR Back scatter for Hydrology Applications. Canadian Joural of Remote Sensing. 1995,22(1):65-76.
[5]H Skriver, J K Ji, J Dall, K Woelders, A Thomsen. A Multi-Temporal and Multi-Frequency Study of Polarimetric Signatures of Soil and Crops. European Conference on Synthetic Aperture Radar,Germany,1996:481-484.
[6]E Candes. Compressive sampling[A]. Proceedings of the International Congress of Mathematicians. Madrid,2006,3:1433-1452.
[7]D L Donoho. Compressed sensing[J]. IEEE Trans. On Information Theory. 2006,52(4):1289-1306.
[8]R Baraniuk. Compressive sensing[A]. IEEE Signal Processing[C].2007(4),7:118-121.
[9]R G Baraniuk, P Steeghs. Compressive Radar Imaging[A]. IEEE Radar Conference,Waltham,MA,2007,4.
[10]焦李成,谭山.图像的多尺度几何分析：回顾和展望[J]. 电子学报.2003,31(12A)：1975-1981.
[11]E L Pennee,S Mallat. Image compression with geometrical wavelets[A]. Proc. of IEEE Internations Conference on Image Processing, ICIP'2000[C]. Vancotwer,BC:IEEE Computer Society,2000.1:661-664.
[12]张玉英,侯新宇,张晓金,郭华昌.相位转换平面的吸波原理分析及应用.电子测量技术,2007,30(8).
[13]H C Stankwitz, S P Taylor. Advances in non-linear apodization. IEEE Aerospace and Electronic Systems Magazine.2006,21(1):3-8.
[14]H C Stankwitz, R J Dallaire, J R Fienup. Nonlinear apodization for sidelobe control in SAR imagery. IEEE Transactions on Aerospace and Electronic Systems. 1995,31(1):267-279.
[15]X Xu, Narayanan R M. Enhanced resolution in SAR/ISAR imaging using iterative sidelobe apodization. Image Processing,IEEE Transactions on.2005,14(4):537-547.
[16]李真芳,保铮.基于成像的分布式卫星SAR系统地面运动目标检测及定位技术[A].中国科学E辑,信息科学.2005,35(6)：597-609.
[17]李海英.超宽带信号波形及其合成孔径雷达成像研究[D].中国科学院研究生院(电子学研究所),2002.
[18]Busby H R, Trujillo D M. Optimal regularization of an inverse dynamics problem[J]. Computers&Structures,1997,63(2):243-248.
[19]石光明,刘丹华,高大化,刘哲等.压缩感知理论及其研究进展[A].电子学报,2009,37(5).
[20]Card,S E J. Ridgelets:theory and applicatiom[D], Stanford; Stanford University.1998.
[21]E Candes, D L Donoho. Curvelets[R]. USA.Department of Statistics, Stanford University.1999.
[22]E L Pennee, S Mallat. Image compression with geometrical wavelets[A]. Proc.of IEEE International Conference on Image Processing, ICIP'2000[C]. Vancotwer,BC:IEEE Computer Society,2000,1:661-664.
[23]D Mirth N, Vetterti, Martin. Contourlets:A new directional multiresolution image representation [A]. Conference Record of the Asilomar Conference on Signals,Systems and Computers[C]. Pacific Groove, CA,United States:IEEE Computer Society,2002,1:497-501.
[24]D L Donoho. Compressed sensing[J]. IEEE Trans. On Information Theory. 2006,52(4):1289-1306.
[25]E Candes, J Romberg, Terence Tao. Robust uncertainty principles:Exact signal reconstruction form highly incomplete frequency information[J]. IEEE Trans. On Information Theory,2006,52(2):489-509.
[26]G Peyr e. Best Basis compressed sensing[J]. Lecture Notes in Computer Science,2007,4485:80-91.
[27]S Mallat, Z Zhang. Matching pursuits with time-frequency dictionaries[J]. IEEE Trans Signal Process,1993,41(12):3397-3415.
[28]E Candes. The restricted isometry property and its implications for compressed sensing[J]. Academie des sciences,2006,346(1):598-592.
[29]R Baraniuk. A lecture on compressive sensing[J]. IEEE Signal Processing Magazine,2007,24(4):118-121.
[30]J A Tropp, A C Gilbert. Signal Recovery from Partial Information by Orthogonal Matching Pursuit[OL]. www-personal. umich.edu/_jtropp-/papers/TG05-Signal-Recovery.pdf.2005,4.
[31]D Needell, R Vershynin. Signal recovery from incomplete and inaccurate measurements via regularized orthogonal matching pursuit[OL]. http://www.math.ucdavis.edu/ %7Evershynin/papers/ROMP-stability.pdf.
[32]A C Gilbert, M J Strauss, J A Tropp,et. One sketch for all:Fast algorithms for compressed sensing [A]. Proceedings of the 39th Annual ACM Symposium on Theory of Computing[C]. Association for Computing Machiner,2007:237-246.
[33]张直中.合成孔径雷达遥感技术及其应用.电子工程师,1997,23(4)：2-11.
[34]张直中.军民两用的AN/APG-76(V)机载雷达.现代雷达,1999,21(6)：1-4.
[35]刘光平.超宽带合成孔径雷达高效成像算法[D].中国人民解放军国防科学技术大学,2003年.
[36]林翊青,李景文.大距离徙动情况下距离多普勒算法与后向投影(BP)算法的比较[A].雷达科学与技术,2004,6.
[37]周荫清,何岷,陈杰,李春升.基于星上实时信号处理机的Chirp Scaling算法实现方法[J].北京航空航天大学学报,2005,2.
[38]L Ulander, H Hellsten. System Analysis of Ultra Wideband VHF SAR. Radar, 1997,10:104-108.
[39]G E Haslam, et al. A High Resolution Multimode Synthetic Aperture Radar. Radar Cont,1992:371-374.
[40]J Li, P Stoica. An adaptive filtering approach to spectral estimation and SAR imaging. IEEE Trans. On Signal Processing,1996(b),44(6):1469-1484.
[41]王光新,王正明.SAR图像目标超分辨的变范数正则化算法[A].电子学报,2008,12.
[42]Zhao Q B, Jian Li, Zheng She Liu. Super resolution SAR imaging via parametric spectral estimation methods [J]. IEEE Transactions on Aerospace and Electronic Systems,1999,35(1):267-281.
[43]曾三友,丁立新等.一种具有噪声估计能力的图像恢复正则化方法[J].武汉大学学报：理学版,2002,48(3)：311-314.
[44]Chengbo Li, Wotao Yin, Yin Zhang. Augmented Lagrangian and ALternating direction Algorithms[OL]. http://www.caam.rice.edu/-optimization/L1/TVAL3/.
[45]D C Munson Jr, J L C Sanz. Image reconstruction from frequency-offset Fourier data. IEEEProc.1984,72(6):661-669.
[46]M Duarte. Fast reconstruction from random incoherent projections. Rice ECE Department Technical Report TREE 0507.2005,5.
[47]M Cetin, W C Karl. Feature-enhanced synthetic aperture radar image formation based on nonquadratic regularization. IEEE Trans. Image Processing.2001,10(4):623-631.
[48]S Samadi, M Cetin, M A Masnadi-Shirazi. Sparse Signal Representation for Complex-Valued Imaging[A]. IEEE Signal Processing.2009,365-370.
[49]M Cetin, W C Karl, A S Willsky. Feature-preserving regularization method for complex-valued inverse problems with application to coherent imaging, Optical Engineering.45(1) 2006,1:017003.
[2]袁小金.合成孔径雷达图像提高分辨率技术研究[D].上海交通大学硕士论文,2007.
[3]J B Boisvert, T J Pultz, R J Brown, B Brisco. Potential of Synthetic Aperture Radar for Large-Scale Soil Moisture Monitoring:A Review. Canadian Journal of Remote Sensing. 1995,22(1):2-13.
[4]Y Grevier,T J Pultz, T I Lukowski, T Toutin. Temporal Analysis of ERS-1 SAR Back scatter for Hydrology Applications. Canadian Joural of Remote Sensing. 1995,22(1):65-76.
[5]H Skriver, J K Ji, J Dall, K Woelders, A Thomsen. A Multi-Temporal and Multi-Frequency Study of Polarimetric Signatures of Soil and Crops. European Conference on Synthetic Aperture Radar,Germany,1996:481-484.
[6]E Candes. Compressive sampling[A]. Proceedings of the International Congress of Mathematicians. Madrid,2006,3:1433-1452.
[7]D L Donoho. Compressed sensing[J]. IEEE Trans. On Information Theory. 2006,52(4):1289-1306.
[8]R Baraniuk. Compressive sensing[A]. IEEE Signal Processing[C].2007(4),7:118-121.
[9]R G Baraniuk, P Steeghs. Compressive Radar Imaging[A]. IEEE Radar Conference,Waltham,MA,2007,4.
[10]焦李成,谭山.图像的多尺度几何分析：回顾和展望[J]. 电子学报.2003,31(12A)：1975-1981.
[11]E L Pennee,S Mallat. Image compression with geometrical wavelets[A]. Proc. of IEEE Internations Conference on Image Processing, ICIP'2000[C]. Vancotwer,BC:IEEE Computer Society,2000.1:661-664.
[12]张玉英,侯新宇,张晓金,郭华昌.相位转换平面的吸波原理分析及应用.电子测量技术,2007,30(8).
[13]H C Stankwitz, S P Taylor. Advances in non-linear apodization. IEEE Aerospace and Electronic Systems Magazine.2006,21(1):3-8.
[14]H C Stankwitz, R J Dallaire, J R Fienup. Nonlinear apodization for sidelobe control in SAR imagery. IEEE Transactions on Aerospace and Electronic Systems. 1995,31(1):267-279.
[15]X Xu, Narayanan R M. Enhanced resolution in SAR/ISAR imaging using iterative sidelobe apodization. Image Processing,IEEE Transactions on.2005,14(4):537-547.
[16]李真芳,保铮.基于成像的分布式卫星SAR系统地面运动目标检测及定位技术[A].中国科学E辑,信息科学.2005,35(6)：597-609.
[17]李海英.超宽带信号波形及其合成孔径雷达成像研究[D].中国科学院研究生院(电子学研究所),2002.
[18]Busby H R, Trujillo D M. Optimal regularization of an inverse dynamics problem[J]. Computers&Structures,1997,63(2):243-248.
[19]石光明,刘丹华,高大化,刘哲等.压缩感知理论及其研究进展[A].电子学报,2009,37(5).
[20]Card,S E J. Ridgelets:theory and applicatiom[D], Stanford; Stanford University.1998.
[21]E Candes, D L Donoho. Curvelets[R]. USA.Department of Statistics, Stanford University.1999.
[22]E L Pennee, S Mallat. Image compression with geometrical wavelets[A]. Proc.of IEEE International Conference on Image Processing, ICIP'2000[C]. Vancotwer,BC:IEEE Computer Society,2000,1:661-664.
[23]D Mirth N, Vetterti, Martin. Contourlets:A new directional multiresolution image representation [A]. Conference Record of the Asilomar Conference on Signals,Systems and Computers[C]. Pacific Groove, CA,United States:IEEE Computer Society,2002,1:497-501.
[24]D L Donoho. Compressed sensing[J]. IEEE Trans. On Information Theory. 2006,52(4):1289-1306.
[25]E Candes, J Romberg, Terence Tao. Robust uncertainty principles:Exact signal reconstruction form highly incomplete frequency information[J]. IEEE Trans. On Information Theory,2006,52(2):489-509.
[26]G Peyr e. Best Basis compressed sensing[J]. Lecture Notes in Computer Science,2007,4485:80-91.
[27]S Mallat, Z Zhang. Matching pursuits with time-frequency dictionaries[J]. IEEE Trans Signal Process,1993,41(12):3397-3415.
[28]E Candes. The restricted isometry property and its implications for compressed sensing[J]. Academie des sciences,2006,346(1):598-592.
[29]R Baraniuk. A lecture on compressive sensing[J]. IEEE Signal Processing Magazine,2007,24(4):118-121.
[30]J A Tropp, A C Gilbert. Signal Recovery from Partial Information by Orthogonal Matching Pursuit[OL]. www-personal. umich.edu/_jtropp-/papers/TG05-Signal-Recovery.pdf.2005,4.
[31]D Needell, R Vershynin. Signal recovery from incomplete and inaccurate measurements via regularized orthogonal matching pursuit[OL]. http://www.math.ucdavis.edu/ %7Evershynin/papers/ROMP-stability.pdf.
[32]A C Gilbert, M J Strauss, J A Tropp,et. One sketch for all:Fast algorithms for compressed sensing [A]. Proceedings of the 39th Annual ACM Symposium on Theory of Computing[C]. Association for Computing Machiner,2007:237-246.
[33]张直中.合成孔径雷达遥感技术及其应用.电子工程师,1997,23(4)：2-11.
[34]张直中.军民两用的AN/APG-76(V)机载雷达.现代雷达,1999,21(6)：1-4.
[35]刘光平.超宽带合成孔径雷达高效成像算法[D].中国人民解放军国防科学技术大学,2003年.
[36]林翊青,李景文.大距离徙动情况下距离多普勒算法与后向投影(BP)算法的比较[A].雷达科学与技术,2004,6.
[37]周荫清,何岷,陈杰,李春升.基于星上实时信号处理机的Chirp Scaling算法实现方法[J].北京航空航天大学学报,2005,2.
[38]L Ulander, H Hellsten. System Analysis of Ultra Wideband VHF SAR. Radar, 1997,10:104-108.
[39]G E Haslam, et al. A High Resolution Multimode Synthetic Aperture Radar. Radar Cont,1992:371-374.
[40]J Li, P Stoica. An adaptive filtering approach to spectral estimation and SAR imaging. IEEE Trans. On Signal Processing,1996(b),44(6):1469-1484.
[41]王光新,王正明.SAR图像目标超分辨的变范数正则化算法[A].电子学报,2008,12.
[42]Zhao Q B, Jian Li, Zheng She Liu. Super resolution SAR imaging via parametric spectral estimation methods [J]. IEEE Transactions on Aerospace and Electronic Systems,1999,35(1):267-281.
[43]曾三友,丁立新等.一种具有噪声估计能力的图像恢复正则化方法[J].武汉大学学报：理学版,2002,48(3)：311-314.
[44]Chengbo Li, Wotao Yin, Yin Zhang. Augmented Lagrangian and ALternating direction Algorithms[OL]. http://www.caam.rice.edu/-optimization/L1/TVAL3/.
[45]D C Munson Jr, J L C Sanz. Image reconstruction from frequency-offset Fourier data. IEEEProc.1984,72(6):661-669.
[46]M Duarte. Fast reconstruction from random incoherent projections. Rice ECE Department Technical Report TREE 0507.2005,5.
[47]M Cetin, W C Karl. Feature-enhanced synthetic aperture radar image formation based on nonquadratic regularization. IEEE Trans. Image Processing.2001,10(4):623-631.
[48]S Samadi, M Cetin, M A Masnadi-Shirazi. Sparse Signal Representation for Complex-Valued Imaging[A]. IEEE Signal Processing.2009,365-370.
[49]M Cetin, W C Karl, A S Willsky. Feature-preserving regularization method for complex-valued inverse problems with application to coherent imaging, Optical Engineering.45(1) 2006,1:017003.