分布式SAR动目标成像技术研究
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摘要
分布式SAR(Synthetic Aperture Radar)是一种崭新的雷达体制,它是由多颗卫星构成的星座,各SAR之间协同合作能够共同完成单星SAR不能完成的任务。分布式SAR通过合理的安排构型能够实现更高分辨率的成像、提高动目标检测性能以及实现地形三维成像。星载分布式SAR可以实现全天候、全天时的对地观测,具有很大的军用和民用价值。
针对分布式SAR动目标成像技术,本文对各环节进行系统的研究。首先提出分布式SAR动目标信号处理的流程,并针对处理过程中遇到的相关问题进行深入的研究。在建立分布式SAR动目标回波信号模型的基础上,对其信号特点进行分析和探讨。
动目标由于自身的运动导致分布式SAR接收的回波信号间的相对频率偏差与静止目标不相同,因此不能根据基线关系确定动目标回波信号间的频谱偏移。对此,本文提出对分布式SAR的图像进行干涉处理,利用干涉相位条纹的瞬时频率精确估计分布式SAR动目标信号的频谱偏移。该方法不仅估计精度高,而且突破以往获取分布式SAR信号频偏必需基线和构型等先验知识的束缚,具有很强的实用性和操作性。在得到分布式SAR动目标信号频偏后,可以通过频谱合成方法融合分布式SAR动目标的信息,扩展动目标信号的频谱,从而提高动目标成像的分辨率。
SAR动目标成像时由于目标运动引起多普勒调频率的变化致使方位压缩失效,在图像中表现为方位散焦。传统的动目标聚焦思路是通过估计动目标的参数调整方位匹配压缩函数完成动目标聚焦的。本文深入研究动目标散焦图像中存在的相位误差,提出对散焦图像数据进行谱分解处理得到实现动目标聚焦的相位校正函数,从而完成动目标聚焦。谱分解聚焦(SDF)方法能够避免动目标参数估计等中间环节引入的积累误差,进而提高聚焦效率和聚焦质量。
分布式SAR成像通过信号处理技术可以提高成像分辨率,但在目标相距很近时容易出现目标信号相互干扰的现象,降低图像质量。对此,本文采用幅度相位谱估计方法替代传统FFT估计信号频谱,并利用InSAR干涉技术将其推广至可估计分布式SAR融合信号频谱的干涉幅度相位谱估计(IAPES)方法,如此能够提高谱估计的分辨率并降低旁瓣,从而消除目标间的相互干扰,进一步提高图像的质量。
Distributed small satellite SAR is a new radar implementation, which is consisted of many several satellite worked cooperatively. Distributed satellite SAR can achieve more high resolution image through spectrum fusion, improve moving targets detection probability and reconstruct three dimensions terrain by InSAR interferometric technology. Small spaceborne system can work all-weather and all-day, which is a strong advantage in military or civil field.
In this dissertation, moving targets imaging process in distributed small satellite SAR is proposed firstly. Based on the signal formation established in this paper, the features of moving targets signals is researched and analysis. The research of multi-satellite sample and its signal reconstruction method can provide the theory foundation for moving targets imaging and focusing.
The signal received from multiple SAR has spectrum deviation, but moving targets could change the deviation formation. Hence it can not determine the moving target echo signal spectrum shift from the baseline relationship. A method estimated spectrum shift from the instantaneous frequency of the interferometric phase fringe in distributed SAR moving target signal is proposed. The method not only has high precision of estimation, but also broke the prior knowledge of the distributed configuration and the baseline. After obtained the frequency offset in the SAR moving target signal, we can integer the spectrum, to extend the target signal spectrum to improve the resolution of moving targets imaging.
Target motion changing the Doppler parameters leads to azimuth compression failure, which called the image defocus performance. The traditional idea of focusing moving targets is estimating the parameters of moving target to adjust the azimuth compression function to finish moving target focusing. After studies the moving target defocusing phase error existing in the defocus image data, a method focusing moving target through spectral decomposition of the defocus image data to achieve the phase correction function to completed focusing is proposed. The method avoids the estimation of moving target parameters which accumulating errors and improve focus efficiency as well as the quality of the focusing.
Through signal processing techniques, distributed SAR can improve the imaging resolution, but the image quality will be reduced when the targets closed each other. In this regard, the proposed spectrum estimation method using amplitude and phase estimation signal instead of the traditional FFT spectrum, and extended it to estimate the spectrum of distributed SAR fusion signal based on InSAR interferometry. The method can improve the spectral resolution and lower sid elobes, thereby eliminating interference between targets and further improve image quality.
针对分布式SAR动目标成像技术,本文对各环节进行系统的研究。首先提出分布式SAR动目标信号处理的流程,并针对处理过程中遇到的相关问题进行深入的研究。在建立分布式SAR动目标回波信号模型的基础上,对其信号特点进行分析和探讨。
动目标由于自身的运动导致分布式SAR接收的回波信号间的相对频率偏差与静止目标不相同,因此不能根据基线关系确定动目标回波信号间的频谱偏移。对此,本文提出对分布式SAR的图像进行干涉处理,利用干涉相位条纹的瞬时频率精确估计分布式SAR动目标信号的频谱偏移。该方法不仅估计精度高,而且突破以往获取分布式SAR信号频偏必需基线和构型等先验知识的束缚,具有很强的实用性和操作性。在得到分布式SAR动目标信号频偏后,可以通过频谱合成方法融合分布式SAR动目标的信息,扩展动目标信号的频谱,从而提高动目标成像的分辨率。
SAR动目标成像时由于目标运动引起多普勒调频率的变化致使方位压缩失效,在图像中表现为方位散焦。传统的动目标聚焦思路是通过估计动目标的参数调整方位匹配压缩函数完成动目标聚焦的。本文深入研究动目标散焦图像中存在的相位误差,提出对散焦图像数据进行谱分解处理得到实现动目标聚焦的相位校正函数,从而完成动目标聚焦。谱分解聚焦(SDF)方法能够避免动目标参数估计等中间环节引入的积累误差,进而提高聚焦效率和聚焦质量。
分布式SAR成像通过信号处理技术可以提高成像分辨率,但在目标相距很近时容易出现目标信号相互干扰的现象,降低图像质量。对此,本文采用幅度相位谱估计方法替代传统FFT估计信号频谱,并利用InSAR干涉技术将其推广至可估计分布式SAR融合信号频谱的干涉幅度相位谱估计(IAPES)方法,如此能够提高谱估计的分辨率并降低旁瓣,从而消除目标间的相互干扰,进一步提高图像的质量。
Distributed small satellite SAR is a new radar implementation, which is consisted of many several satellite worked cooperatively. Distributed satellite SAR can achieve more high resolution image through spectrum fusion, improve moving targets detection probability and reconstruct three dimensions terrain by InSAR interferometric technology. Small spaceborne system can work all-weather and all-day, which is a strong advantage in military or civil field.
In this dissertation, moving targets imaging process in distributed small satellite SAR is proposed firstly. Based on the signal formation established in this paper, the features of moving targets signals is researched and analysis. The research of multi-satellite sample and its signal reconstruction method can provide the theory foundation for moving targets imaging and focusing.
The signal received from multiple SAR has spectrum deviation, but moving targets could change the deviation formation. Hence it can not determine the moving target echo signal spectrum shift from the baseline relationship. A method estimated spectrum shift from the instantaneous frequency of the interferometric phase fringe in distributed SAR moving target signal is proposed. The method not only has high precision of estimation, but also broke the prior knowledge of the distributed configuration and the baseline. After obtained the frequency offset in the SAR moving target signal, we can integer the spectrum, to extend the target signal spectrum to improve the resolution of moving targets imaging.
Target motion changing the Doppler parameters leads to azimuth compression failure, which called the image defocus performance. The traditional idea of focusing moving targets is estimating the parameters of moving target to adjust the azimuth compression function to finish moving target focusing. After studies the moving target defocusing phase error existing in the defocus image data, a method focusing moving target through spectral decomposition of the defocus image data to achieve the phase correction function to completed focusing is proposed. The method avoids the estimation of moving target parameters which accumulating errors and improve focus efficiency as well as the quality of the focusing.
Through signal processing techniques, distributed SAR can improve the imaging resolution, but the image quality will be reduced when the targets closed each other. In this regard, the proposed spectrum estimation method using amplitude and phase estimation signal instead of the traditional FFT spectrum, and extended it to estimate the spectrum of distributed SAR fusion signal based on InSAR interferometry. The method can improve the spectral resolution and lower sid elobes, thereby eliminating interference between targets and further improve image quality.
引文
[1]林来兴.分布式小卫星系统的技术发展与应用前景[J].航天器工程, 2010, 19(1):60-66
[2]马仑.分布式小卫星SAR宽域、高分辨率成像方法研究[D].西安:西安电子科技大学博士学位论文. 2008:1-5
[3] Nathan A. Goodman. Resolution and Synthetic Aperture Characterization of Sparse Radar Arrays[J]. IEEE Transactions on Aerospace and Electronic Systems, 2003, 39(3):921-935
[4] Nathan A. Goodman, Sih Chung Lin. Processing of Multiple-Receiver Spaceborne Arrays for Wide-Area SAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(4):841–849
[5]李真芳,邢孟道,王彤等.分布式小卫星SAR实现全孔径分辨率的信号处理[J].电子学报, 2003, 31(12):1800-1803
[6] A. Currie, M. A. Brown. Wider-Swath SAR[J]. IEEE Proceedings, Part F: Radar and Signal Processing, 1992, 139(2):122-135
[7] Didier Massonnet. Capabilities and Limitations of the Interferometric Cartwheel[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(3):506-520
[8] C. Prati, E. Rocca. Improving Slant-Range Resolution With Multiple SAR Surveys [J]. IEEE Transactions on Aerospace and Electronic Systems, 1993, 29(1):135-144
[9]江碧涛.分布式雷达成像及地面云动目标检测方法研究[D].北京:中国科学院空间科学与应用研究中心博士学位论文. 2007:4-7
[10] Burns, McLaughlin. TechSat 21: Formation design, control, and simulation[C]. IEEE Aerospace Conference Proceedings, 2000, 7:19-25
[11] M Martin, P Klupar. Techsat 21 and Revolutionizing Space Missions using Microsatellites[C]. Proc. 15th AIAA Conference on Small Satellites, Utah, USA, 2001:4139-4145
[12] Massonnet. The interferometric cartwheel: A constellation of passive satellites to produce radar images to be coherently combined[J]. International Journal of Remote Sensing, 2001, 22(12):2413-2430
[13] Schwerdt Marco, Hounam David. TerraSAR-X: Calibration concept of a multiple mode high resolution SAR[R]. International Geoscience and Remote Sensing Symposium(IGARSS), 2005:4874-4877
[14] Moreira Alberto, Krieger Gerhard. TanDEM-X: A TerraSAR-X add-on satellite for single-pass SAR interferometry[R]. International Geoscience and Remote Sensing Symposium(IGARSS), 2004:1000-1003
[15]闫鸿慧,王岩飞.利用频谱合成实现分布式卫星SAR高距离分辨力成像[J].电子与信息学报, 2005, 27(6):928-931
[16]徐华平,周荫清,李春升.基于频率偏移估计的分布式星载SAR提高距离向分辨率的数据处理方法[J].电子学报, 2003, 31(12):1790-1794
[17]周峰,李亚超,邢孟道等.一种单通道SAR地面运动目标成像和运动参数估计方法[J].电子学报, 2007, 35(3):543-548
[18] Robert L. Morrison, Minh N. Do, David C. Munson. MCA: A Multichannel Approach to SAR Autofocus[J]. IEEE Transactions on Image Processing, 2009, 18(4):840-853
[19]陈轶,金亚秋. SAR图像中运动目标重聚焦改进的最小熵方法[J].电子与信息学报, 2003, 25(2):263-269
[20]李燕平,邢孟道,保铮.一种改进的相位梯度聚焦算法[J].西安电子科技大学学报, 2007, 34(3):386-391
[21] Qian Jiang, Wang Yu, Li Jianghai. Doppler Ambiguity Resolving for SAR Ground Fast Moving Target Indication[C]. IEEE Asian Pacific Conference on Synthetic Aperture Radar, Xian Shanxi, China, Oct 26-30, 2009:201-205
[22] Jian Li, Petre Stoica. An adaptive filtering approach to spectral estimation and SAR imaging[J]. IEEE Trans on Signal Processing, 1996, 44(6):1469-1484
[23] Marzban R Palsetia. Using APES for interferometric SAR imaging[J]. IEEE Trans on Image Processing, 1998, 7(9):1340-1353
[24] M.Martin, M.Stallard. Distributed satellite missions and technologies-the TechSat21 program[D]. Proc. AIAA Space Technology Conf. Exposition, Albuquerque, NM, Sept. 1999:4476-4479
[25]钱江,吕孝雷,邢孟道等.机载三通道SAR/GMTI快速目标运动参数估计[J].西安电子科技大学学报, 2010, 37(2):235-241
[26]保铮,邢孟道.雷达成像技术[M].北京:电子工业出版社, 2004:298-301
[27]左艳军,分布式小卫星合成孔径雷达高分辨成像算法研究[D].北京:中国科学院电子学研究所博士论文, 2007:48-59
[28]闫鸿慧,王岩飞.利用频谱合成实现分布式SAR高分辨力成像[J].电子与信息学报, 2006, 28(2):345-349
[29]禹卫东,用分布式小卫星提高星载SAR的方位向分辨率[J].系统工程与电子技术, 2002, 24(7):43-46
[30] Kuang HungLiu, David C. Munson. Autofocus in Multistatic Passive SAR Imaging[C]. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2008:1277-1280
[31] Shengqi Zhu, Guisheng Liao. Ground Moving Targets Imaging Algorithm for Synthetic Aperture Radar[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010:1-16
[32] Ivana Stojanovic, William Clem Karl. Imaging of Moving Targets With Multi-Static SAR Using an Overcomplete Dictionary[J]. IEEE Journal of Selected Topics in Signal Processing, 2010, 4(1):164-176
[33] Tommy Teer, Nathan A. Goodman. Multistatic SAR Algorithm with Image Combination[C]. IEEE Conference on Radar, 2006:490-497
[34] Gang Li, Xianggen Xia, Yingning Peng. Doppler Keystone Transform for SAR Imaging of Moving Targets[C]. IEEE Congress on Image and Signal Processing, 2008:716-719
[35]邱晓辉,赵阳. ISAR成像最小熵自聚焦与相位补偿的一致性分析[J].电子与信息学报, 2007, 29(8):1799-1801
[36]刘碧丹,韩松,王岩飞.图像幅度和值最小化子聚焦算法[J].电子与信息学报, 2009, 31(4):768-771
[37]邓云凯,王宇,杨贤林.基于对比度最优准则的自聚焦优化算法研究[J].电子学报, 2006, 34(9):1742-1744
[38]李明,王彤.单天线SAR动目标的检测和聚焦[J].系统工程与电子技术, 2008, 30(9):1645-1648
[39]王勇,姜义成. Capon方法在InSAR成像中的应用[J].哈尔滨工业大学学报, 2007, 39(11):1752-1755
[40] Petre Stoica, Hongbin Li, Jian Li. A New Derivation of the APES Filter[J]. IEEE Signal Processing Letters, 1999, 6(8):205-206
[41] Erik G. Larsson, Petre Stoica, Jian Li. Amplitude Spectrum Estimation for Two-Dimensional Gapped Data[J]. IEEE Transactions on Signal Processing, 2002,50(6):1343-1354
[42] Jian Li, Petre Stoica, Zhisong Wang. On Robust Capon Beamforming and Diagonal Loading[J]. IEEE Transactions on Signal Processing, 2003, 51(7):1702-1715
[2]马仑.分布式小卫星SAR宽域、高分辨率成像方法研究[D].西安:西安电子科技大学博士学位论文. 2008:1-5
[3] Nathan A. Goodman. Resolution and Synthetic Aperture Characterization of Sparse Radar Arrays[J]. IEEE Transactions on Aerospace and Electronic Systems, 2003, 39(3):921-935
[4] Nathan A. Goodman, Sih Chung Lin. Processing of Multiple-Receiver Spaceborne Arrays for Wide-Area SAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(4):841–849
[5]李真芳,邢孟道,王彤等.分布式小卫星SAR实现全孔径分辨率的信号处理[J].电子学报, 2003, 31(12):1800-1803
[6] A. Currie, M. A. Brown. Wider-Swath SAR[J]. IEEE Proceedings, Part F: Radar and Signal Processing, 1992, 139(2):122-135
[7] Didier Massonnet. Capabilities and Limitations of the Interferometric Cartwheel[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(3):506-520
[8] C. Prati, E. Rocca. Improving Slant-Range Resolution With Multiple SAR Surveys [J]. IEEE Transactions on Aerospace and Electronic Systems, 1993, 29(1):135-144
[9]江碧涛.分布式雷达成像及地面云动目标检测方法研究[D].北京:中国科学院空间科学与应用研究中心博士学位论文. 2007:4-7
[10] Burns, McLaughlin. TechSat 21: Formation design, control, and simulation[C]. IEEE Aerospace Conference Proceedings, 2000, 7:19-25
[11] M Martin, P Klupar. Techsat 21 and Revolutionizing Space Missions using Microsatellites[C]. Proc. 15th AIAA Conference on Small Satellites, Utah, USA, 2001:4139-4145
[12] Massonnet. The interferometric cartwheel: A constellation of passive satellites to produce radar images to be coherently combined[J]. International Journal of Remote Sensing, 2001, 22(12):2413-2430
[13] Schwerdt Marco, Hounam David. TerraSAR-X: Calibration concept of a multiple mode high resolution SAR[R]. International Geoscience and Remote Sensing Symposium(IGARSS), 2005:4874-4877
[14] Moreira Alberto, Krieger Gerhard. TanDEM-X: A TerraSAR-X add-on satellite for single-pass SAR interferometry[R]. International Geoscience and Remote Sensing Symposium(IGARSS), 2004:1000-1003
[15]闫鸿慧,王岩飞.利用频谱合成实现分布式卫星SAR高距离分辨力成像[J].电子与信息学报, 2005, 27(6):928-931
[16]徐华平,周荫清,李春升.基于频率偏移估计的分布式星载SAR提高距离向分辨率的数据处理方法[J].电子学报, 2003, 31(12):1790-1794
[17]周峰,李亚超,邢孟道等.一种单通道SAR地面运动目标成像和运动参数估计方法[J].电子学报, 2007, 35(3):543-548
[18] Robert L. Morrison, Minh N. Do, David C. Munson. MCA: A Multichannel Approach to SAR Autofocus[J]. IEEE Transactions on Image Processing, 2009, 18(4):840-853
[19]陈轶,金亚秋. SAR图像中运动目标重聚焦改进的最小熵方法[J].电子与信息学报, 2003, 25(2):263-269
[20]李燕平,邢孟道,保铮.一种改进的相位梯度聚焦算法[J].西安电子科技大学学报, 2007, 34(3):386-391
[21] Qian Jiang, Wang Yu, Li Jianghai. Doppler Ambiguity Resolving for SAR Ground Fast Moving Target Indication[C]. IEEE Asian Pacific Conference on Synthetic Aperture Radar, Xian Shanxi, China, Oct 26-30, 2009:201-205
[22] Jian Li, Petre Stoica. An adaptive filtering approach to spectral estimation and SAR imaging[J]. IEEE Trans on Signal Processing, 1996, 44(6):1469-1484
[23] Marzban R Palsetia. Using APES for interferometric SAR imaging[J]. IEEE Trans on Image Processing, 1998, 7(9):1340-1353
[24] M.Martin, M.Stallard. Distributed satellite missions and technologies-the TechSat21 program[D]. Proc. AIAA Space Technology Conf. Exposition, Albuquerque, NM, Sept. 1999:4476-4479
[25]钱江,吕孝雷,邢孟道等.机载三通道SAR/GMTI快速目标运动参数估计[J].西安电子科技大学学报, 2010, 37(2):235-241
[26]保铮,邢孟道.雷达成像技术[M].北京:电子工业出版社, 2004:298-301
[27]左艳军,分布式小卫星合成孔径雷达高分辨成像算法研究[D].北京:中国科学院电子学研究所博士论文, 2007:48-59
[28]闫鸿慧,王岩飞.利用频谱合成实现分布式SAR高分辨力成像[J].电子与信息学报, 2006, 28(2):345-349
[29]禹卫东,用分布式小卫星提高星载SAR的方位向分辨率[J].系统工程与电子技术, 2002, 24(7):43-46
[30] Kuang HungLiu, David C. Munson. Autofocus in Multistatic Passive SAR Imaging[C]. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2008:1277-1280
[31] Shengqi Zhu, Guisheng Liao. Ground Moving Targets Imaging Algorithm for Synthetic Aperture Radar[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010:1-16
[32] Ivana Stojanovic, William Clem Karl. Imaging of Moving Targets With Multi-Static SAR Using an Overcomplete Dictionary[J]. IEEE Journal of Selected Topics in Signal Processing, 2010, 4(1):164-176
[33] Tommy Teer, Nathan A. Goodman. Multistatic SAR Algorithm with Image Combination[C]. IEEE Conference on Radar, 2006:490-497
[34] Gang Li, Xianggen Xia, Yingning Peng. Doppler Keystone Transform for SAR Imaging of Moving Targets[C]. IEEE Congress on Image and Signal Processing, 2008:716-719
[35]邱晓辉,赵阳. ISAR成像最小熵自聚焦与相位补偿的一致性分析[J].电子与信息学报, 2007, 29(8):1799-1801
[36]刘碧丹,韩松,王岩飞.图像幅度和值最小化子聚焦算法[J].电子与信息学报, 2009, 31(4):768-771
[37]邓云凯,王宇,杨贤林.基于对比度最优准则的自聚焦优化算法研究[J].电子学报, 2006, 34(9):1742-1744
[38]李明,王彤.单天线SAR动目标的检测和聚焦[J].系统工程与电子技术, 2008, 30(9):1645-1648
[39]王勇,姜义成. Capon方法在InSAR成像中的应用[J].哈尔滨工业大学学报, 2007, 39(11):1752-1755
[40] Petre Stoica, Hongbin Li, Jian Li. A New Derivation of the APES Filter[J]. IEEE Signal Processing Letters, 1999, 6(8):205-206
[41] Erik G. Larsson, Petre Stoica, Jian Li. Amplitude Spectrum Estimation for Two-Dimensional Gapped Data[J]. IEEE Transactions on Signal Processing, 2002,50(6):1343-1354
[42] Jian Li, Petre Stoica, Zhisong Wang. On Robust Capon Beamforming and Diagonal Loading[J]. IEEE Transactions on Signal Processing, 2003, 51(7):1702-1715