摘要
线阵三维SAR作为一种新型的三维雷达成像技术,在高精度测绘与资源调查、自然灾害监测与侦察预警等民用和军用领域都有广泛及重要的应用价值。受限于传统Nyquist采样定理和经典信号处理理论,目前线阵三维SAR存在成像分辨率过低、硬件系统实现困难、信号采样率过高、回波数据量大、数据的传输、存储以及处理困难等瓶颈难题。压缩传感稀疏重构作为一种近几年新提出的信号处理理论,突破了传统Nyquist采样定理约束,可利用远低于Nyquist采样率精确重构原始稀疏信号,在降低线阵三维SAR系统采样率和提高成像质量等方面有着巨大的应用潜力。本论文以压缩传感稀疏信号处理理论为核心,以高分辨率线阵三维SAR稀疏成像机理与方法作为研究内容,重点围绕线阵三维SAR稀疏成像技术中回波信号线性表征、稀疏重构成像方法、相位误差估计与补偿和线阵阵元分布优化等关键问题展开深入研究。论文的主要工作和创新总结如下：
1.研究线阵三维SAR稀疏成像基本原理。从线阵三维SAR回波信号与成像空间映射关系入手,建立了线阵三维SAR距离向、阵列平面维和全场景三维空间回波信号的线性测量模型,将线阵三维SAR成像处理转化成散射系数线性方程求解问题,为线阵三维SAR成像新方法研究提供了理论基础；构建了基于压缩传感稀疏信号处理理论的三维线阵SAR稀疏成像处理的总体模型,分析了线阵三维SAR成像空间中目标散射系数稀疏表示、回波信号稀疏采样方式、复数域稀疏重构方法以及稀疏成像分辨率特性；针对线阵三维SAR大规模数据处理,提出了阵列平面与距离向分维处理的线阵三维SAR稀疏成像方法；通过理论推导,比较了传统匹配滤波方法、最小二乘方法和压缩传感稀疏重构方法的成像性能。
2.研究了线阵三维SAR稀疏成像算法。将OMP稀疏重构算法应用于线阵三维SAR复数域稀疏成像,分析OMP算法稀疏成像性能；针对线阵三维SAR成像中目标稀疏度未知情况,提出一种硬阈值OMP稀疏成像算法,利用目标散射系数变化率作为算法迭代终止条件,未知目标稀疏度时也能较精确实现线阵三维SAR稀疏成像；将BCS算法应用于线阵三维SAR复数域稀疏成像,分析了BCS算法成像性能；针对BCS算法多个参数选择困难问题,基于目标散射系数指数分布、贝叶斯准则和最大后验估计原理,提出一种基于迭代最小化稀疏贝叶斯重构的线阵三维SAR稀疏成像算法,结合目标散射系数稀疏度估计、自适应参数选择和共轭梯度方法提高了算法稀疏重构性能；利用三维成像空间中目标稀疏特性,将目标位置和散射系数幅度分离进行估计,提出了一种基于稀疏目标区域预测的线阵三维SAR快速稀疏成像算法,通过粗估计稀疏目标位置减少测量矩阵维数,大大减少线阵三维SAR稀疏成像的运算量；另外,结合地基线阵三维SAR实验验证系统和外场实测数据验证了线阵三维SAR稀疏成像技术和稀疏成像方法的有效性。
3.研究了相位误差情况下线阵三维SAR自聚焦稀疏成像方法。分析了不同相位误差对线阵三维SAR稀疏重构成像的影响,建立了不同维向线阵三维SAR相位误差的线性测量模型,将线阵三维SAR相位误差估计和稀疏成像转变为等幅约束线性方程的最优化求解；分析了基于相位误差估计模型松弛的最大似然估计自聚焦算法,比较了特征值松弛方法和半正定松弛方法在线阵三维SAR自聚焦稀疏成像的性能；针对稀疏欠采样情况下的线阵三维SAR回波数据,利用相位误差模型先验分布和贝叶斯准则,提出了一种基于迭代最小化贝叶斯稀疏重构的线阵三维SAR稀疏自聚焦方法,将存在相位误差的线阵三维SAR稀疏成像分解为三个线性最优化求解问题,并利用迭代逼近估计最优稀疏目标系数和误差相位,通过仿真和实测数据验证了算法的有效性。
4.研究了线阵三维SAR稀疏成像中线阵阵元分布优化方法。通过理论推导分析了线阵三维SAR测量矩阵相干系数与系统模糊函数的关系,讨论了非均匀等间隔稀疏和随机稀疏阵元分布对线阵三维SAR测量矩阵相干系数的影响。基于测量矩阵相干系数最小化对线阵三维SAR稀疏成像中线阵阵元分布进行优化设计,提出了基于最小积分旁瓣比的非均匀等间隔稀疏线阵优化方法以及基于最小方差的随机稀疏线阵优化方法,通过仿真数据验证了分布优化方法的有效性。
总之,本文建立了线阵三维SAR稀疏成像技术的基本原理,并在线阵三维SAR稀疏成像方法和阵列优化等方面取得了一系列有价值的研究成果,为新型线阵三维SAR稀疏成像技术研究和应用提供了重要的理论指导和技术支持。
As a novel three-dimensional radar imaging technology, linear array three-dimensional synthetic aperture radar (SAR) has great and important value in military and civilian fields, such as high accuracy mapping, earth resources investigation, disasters and environmental monitoring, reconnaissance and surveillance, early warning, etc.. Limited by the traditional Nyquist sampling theorem and the classical signal processing theory, there exist some problems in the application of LASAR3-D imaging currently, including the low resolution, the high sampling ratio, the difficult of system implementation and the large number of echoes storage, transmission and processing, etc. However, as a new signal processing theorem in recent years, compressed sensing breaks the limits of the classical Nyquist sampling theorem. It can recover a sparse signal exactly with the sampled number far lower than that of the Nyquist ratio, and so has great potential in reducing the radar system sampling and improving the quality of radar imaging. Based on the compressed sensing sparse signal processing theorem, this dissertation focuses on the basic imaging principle and method research for the high resolution3-D LASAR sparse imaging, the key problems mainly including LASAR echoes linear representation, sparse reconstruction method, phase errors correction and array antenna distribution optimization, etc. The main works and innovation points are as follow:
1. Research on the basic principle of LASAR sparse imaging technology. Exploiting the relationship between the LASAR echoes and the imaging space, the linear measurement models of echo signal in range direction, array plane(azimuth-cross-track plane) and the whole3-D image space are constructed respectively, and then LASAR imaging can be converted into a problem where solving the optimal resolution of the given linear equations. These linear models also provide a new idea for LASAR imaging. Further, combined the space sparsity of scatterers, a novel sparse imaging method based on compressed sensing theorem is proposed for LASAR. In addition, the linear sparse representation of scattering coefficients, the sparse sampling of echoes and the resolution of LASAR sparse imaging are discussed. For the large scale data in LASAR, a separable imaging method on range and array plane is proposed for3-D LASAR sparse imaging. Last, the performance of the classical matched filter method, the least square method and the CS sparse recovery method is analyzed through theoretical deducing.
2. Research on sparse reconstruction algorithms for LASAR sparse imaging. First, the classical OMP algorithm is applied to LASAR complex data sparse imaging. For the unknown scatterers sparsity in LASAR imaging, a OMP modified algorithm, named hard threshold OMP (HTOMP) is proposed. By employing the ratio of scattering coefficient change as the iteration stop condition, HTOMP can obtain3-D LASAR image without the scatterer sparsity. Second, the promising BCS algorithm is used for LASAR complex data sparse imaging.in order to reduce the difficult of parameters selection in BCS, base on the exponential distribution of the scattering coefficient, Bayesian theory and maximum likelihood estimation, a sparse Bayesian recovery via iterative minimum (SBRIM) algorithm is proposed for LASAR sparse imaging, wherein, the sparsity estimation method, the adaptive parameter selection method and gradient conjugate method are used to improve the sparse recovery performance. Lastly, combined with the space sparsity of the scatterers in3-D imaging space, a fast sparse recovery method via target location prediction is proposed for LASAR sparse imaging. The effectiveness of LASAR sparse imaging technology and the spare imaging method is verified by some numeral simulation data and the real data obtained ground-based LASAR experimental system.
3. Research on LASAR auto focus sparse imaging algorithm. First, the linear measurement models of LASAR echo signal with phase error for different direction are set up, and the phase error estimation in LASAR can be converted into solving solutions of constrain modulus quadratic program. The effect of the different types of phase errors is discussed. Based on the model relaxation and maximum likelihood estimation, the performances of LASAR sparse autofocus imaging with Eigen-value relaxation and semi-definite relaxation are analyzed. For the under-sampled LASAR echo signal, a novel sparse autofocus Bayesian recovery via iterative minimum algorithm is proposed, wherein, the LASAR autofocus sparse imaging with phase errors can be divided into three steps to finding the optimal solution of the linear equations, and the iterative estimation method is used to obtain the optimal scattering coefficients and the phase error estimation. All algorithms are performed by simulated and real experimental data.
4. Research on the linear array antenna distribution optimization for LASAR sparse imaging. The relationship between the measurement matrix coherence and the LASAR system ambiguity function is studied through theoretical derivations. The effects of the uniform sparse linear array, non-uniform sparse linear array and random sparse linear array for the LASAR measurement matrix are discussed. Based on the minimum measurement matrix coherence, a distribution optimization method based on the minimum peak and the sidelobe ratio is proposed for non-uniform sparse linear array, and a distribution optimization method based on the minimum variance is proposed for random sparse linear array. Simulation results demonstrate the effectiveness of the both methods.
In a word, this dissertation builds the basic principles of LASAR sparse imaging technology, and obtains a series of valuable research results for LASAR sparse imaging algorithm and linear array distribution optimization. The research results provide an important theoretical guidance and technical support for LASAR sparse imaging technology.
引文
[1]F. M. Henderson, J. L. Anthony. Principles and applications of imaging radar. Manual of remote sensing[M]. John Wiley and sons,1998
[2]D. Howard, R. SimonCand B. Richard. Target detection in SAR imagery by genetic programming[J]. Advances in Engineering Software,1999,30(5):303-311
[3]Q. Zhao, J. C. Principe. Support vector machines for SAR automatic target recognition[J]. IEEE Transactions on Aerospace and Electronic Systems,2001,37(2):643-654
[4]M. T. Fennell, R. P. Wishner. Battlefield awareness via synergistic SAR and MTI exploitation[J]. IEEE Transactions on Aerospace and Electronic Systems,1998,13(2):39-43
[5]Y. Nemoto, H. Nishino, M. Ono, et al.. Japanese earth resources satellite-1 synthetic aperture radar[J]. Proceedings of the IEEE,1991,79(6):800-809
[6]M. Matsuoka, F. Yamazaki. Use of satellite SAR intensity imagery for detecting building areas damaged due to earthquakes[J]. Earthquake Spectra,2004,20(3):975-994
[7]L. J. Cutrona. Synthetic aperture radar[M]. Skolnik, McGraw-Hill, New York,1990
[8]M. Soumekh. Synthetic aperture radar signal processing[M]. New York:Wiley,1999
[9]D. R. Wehner. High resolution radar[M]. Norwood, MA, Artech House,1987
[10]C. Elachi, T. Bicknell, R. L. Jordan, et al.. Spaceborne synthetic-aperture imaging radars: Applications techniques and technology[J]. Proceedings of the IEEE,1982,70(10):1174-1209
[11]A. L. Gray, P. J. Farris-Manning. Repeat-pass interferometry with airborne synthetic aperture radar[J]. IEEE Transactions on Geoscience and Remote Sensing,1993,31(1):180-191
[12]I. Walterscheid, T. Espeter, A. R. Brenner, et al.. Bistatic SAR experiments with PAMIR and TerraSAR-X-setup, processing, and image results[J]. IEEE Transactions on Geoscience and Remote Sensing,2010,48(8):3268-3279
[13]J. Horstmann, W. Koch, S. Lehner, et al.. Wind retrieval over the ocean using synthetic aperture radar with C-band HH polarization[J]. IEEE Transactions on Geoscience and Remote Sensing, 2000,38(5):2122-2131
[14]J. Vivekanandan, S. M. Ellis, R. Oye, et al.. Cloud microphysics retrieval using S-band dual-polarization radar measurements[J]. Bulletin of the american meteorological society,1999, 80(3):381-388
[15]M. E. Nord, T. L. Ainsworth, J. S. Lee, et al.. Comparison of compact polarimetric synthetic aperture radar modes[J]. IEEE Transactions on Geoscience and Remote Sensing,2009,47(1): 174-188
[16]M. L. Bryant, L. L. Gostin, M. Soumekh.3-D E-CSAR imaging of a T-72 tank and synthesis of its SAR reconstructions[J]. IEEE Transactions on Aerospace and Electronic Systems,2003, 39(1):211-227
[17]A. Ishimaru, T. K. Chan, Y. Kuga. An imaging technique using confocal circular synthetic aperture radar[J]. IEEE Transactions on Geoscience and Remote Sensing,1998,36(5): 1524-1530
[18]D. C. Munson Jr, J. D. O'Brien, W. K. Jenkins. A tomographic formulation of spotlight-mode synthetic aperture radar[J]. Proceedings of the IEEE,1983,71(8):917-925
[19]L. Du, Y.P. W, W. Hong, et al.. Analytic modeling and three-dimensional imaging of downward-looking SAR using bistatic uniform linear array antennas[C]. IEEE 1st Asian and Pacific Conference on Synthetic Aperture Radar 2007(APSAR2007),2007:49-53.
[20]J. Klare, A. R. Brenner, J. Ender. A new airborne radar for 3D imaging-image formation using the ARTINO principle[C]. EUSAR 2006,2006
[21]M.Weiβ, O. Peters, J. Ender. A three dimensional SAR system on an UAV[C]. IEEE International Geoscience and Remote Sensing Symposium 2007(IGARSS2007),2007: 5315-5318
[22]L. Du, Y. Wang, W. Hong, et al.. A three-dimensional range migration algorithm for downward-looking 3D-SAR with single-transmitting and multiple-receiving linear array antennas[J]. EURASIP Journal on Advances in Signal Processing,2010,2010:11
[23]M. Lustig, D. Donoho, J. M. Pauly. Sparse MRI:the application of compressed sensing for rapid MR imaging[J]. Magnetic Resonance in Medicine,2007,58(6):1182-1195
[24]Z. Xiong, A. D. Liveris, S. Cheng. Distributed source coding for sensor networks[J]. IEEE Signal Processing Magazine,2004,21(5):80-94
[25]M. F. Duarte, M. A. Davenport, D. Takhar, et al.. Single-pixel imaging via compressive sampling[J]. IEEE Signal Processing Magazine,2008,25(2):83-91
[26]M. Lustig, D. Donoho, J. M. Pauly. Sparse MRI:The application of compressed sensing for rapid MR imaging[J]. Magnetic Resonance in Medicine,2007,58(6):1182-1195
[27]J. Wright, Y. Ma, J. Mairal, et al.. Sparse representation for computer vision and pattern recognition[J]. Proceedings of the IEEE,2010,98(6):1031-1044
[28]Z. Tian, G. B. Giannakis. Compressed sensing for wideband cognitive radios[C]. IEEE International Conference on Acoustics, Speech and Signal Processing 2007(ICASSP2007). 2007,4:1357-1360
[29]R. Baraniuk, P. Steeghs. Compressive radar imaging[C]. IEEE Radar Conference2007,2007: 128-133
[30]P. Pasquali, C. Prati, F. Rocca, et al.. A 3-D SAR experiment with EMSL data[C]. IEEE International Geoscience and Remote Sensing Symposium 1995(IGARSS1995),1995,1: 784-786
[31]R. Scheiber, A. Reigber, A. Ulbricht, et al.. Overview of interferometric data acquisition and processing modes of the experimental airborne SAR system of DLR[C]. IEEE International Geoscience and Remote Sensing Symposium 1999(IGARSS1999),1999,1:35-37
[32]G. Fornaro, F. Serafino. Spaceborne 3D SAR tomography:experiments with ERS data[C]. International Geoscience and Remote Sensing Symposium (IGARSS2004), Alaska,2004: 1240-1243
[33]F. Lombardini. Differential tomography:A new framework for SAR interferometry[J]. IEEE Transactions on Geoscience and Remote Sensing,2005,43(1):37-44
[34]O. Stebler, E. Meier, D. Nuesch. Multi-baseline polarimetric SAR interferometry-first experimental spaceborne and airborne results [J]. Journal of Photogrammetry and Remote Sensing,2002,56(3):149-166
[35]R. Andreas, A.Moreira. First demonstration of airborne SAR tomography using multibaseline L-band data[J]. IEEE Transactions on Geoscience and Remote Sensing,2000,38(5) 2142-2152
[36]O. Fery and E. Meier.3-D Time-domain SAR imaging of a forest using airborne multibaseline data at L-and P-bands[J]. IEEE Transactions on Geoscience and Remote Sensing,2011,49(10): 3660-3664
[37]S. Buckreuss, W. Balzer, P. Muhlbauer, et al.. The TerraSAR-X satellite project[C]. IEEE International Geoscience and Remote Sensing Symposium 2003(IGARSS2003),2003,5: 3096-3098
[38]F. Covello, F. Battazza, A. Coletta, et al.. COSMO-SkyMed an existing opportunity for observing the Earth[J]. Journal of Geodynamics,2010,49(3):171-180
[39]G. Krieger, A. Moreira, H. Fiedler, et al..TanDEM-X:a satellite formation for high-resolution SAR interferometry[J]. IEEE Transactions on Geoscience and Remote Sensing,2007,45(11): 3317-3341
[40]G. Fornaro, F. Lombardini, F. Serafino. Three-dimensional multipass SAR focusing: Experiments with long-term spacebornedata[J]. IEEE Transactions on Geoscience and Remote Sensing,2005,43(4):702-714
[41]X. X. Zhu, N. Adam, R. Brcic, et al.. Space-borne high resolution SAR tomography, experiments in urban environment using TS-X Data[C]. IEEE Joint Urban Remote Sensing Event 2009,2009:1-8
[42]T. K. Chan, Y. Kuga, A. Ishimaru. Experimental studies on circular SAR imaging in clutter using angular correlation function technique[J]. IEEE Transactions on Geoscience and Remote Sensing,1999,37(5):2192-2197
[43]M. L. Bryant, L. L. Gostin, M. Soumekh. Three-dimensional E-CSAR imaging of a T-72 tank and synthesis of its spotlight, stripmap and interferometric SAR reconstructions[C]. IEEE International Conference on Image Processing 2001,2001,3:628-631
[44]E. Ertin, C. D. Austin, S. Sharma, et al.. GOTCHA experience report:three-dimensional SAR imaging with complete circular apertures[C]. Defense and Security Symposium. International Society for Optics and Photonics,2007:656802-656802-12
[45]B. R. Mahafza,M. Sajjad. Three-dimensional SAR imaging using linear array in transverse motion[J]. IEEE Transaction on Aerospace and Electronic System.1996,32(1):499-510
[46]Gierull, Christoph H.On a concept for an airborne downward-looking imaging radar[J], AEU-Archiv fur Elektronik und Ubertragungstechnik,1999,53(6):295-304
[47]R. Giret, H. Jeuland and P. Enert. A study of a 3D-SAR concept for a millimeter-wave imaging radar onboard an UAV[C]. IEEE European Radar Conference 2004(EURAD2004), Amsterdam, 2004,201-204
[48]M. Weib, J. H. G. Ender. A 3D imaging radar for small unmanned airplanes-ARTINO[C]. IEEE European Radar Conference 2005(EURAD 2005),2005:209-212
[49]M. Weiβ, O. Peters, J. Ender. First flight trials with ARTINO[C].7th European Conference Synthetic Aperture Radar 2008 (EUSAR2008), European,2008:1-4
[50]J. Klare,D. Cerutti-Maori, A. Brenner, et al.. Image quality analysis of the vibrating sparse MIMO antenna array of the airborne 3D imaging radar ARTINO[C]. IEEE International Geoscience and Remote Sensing Symposium 2007(IGARSS 2007),2007:5310-5314
[51]M. WeiB, M. Gilles. Initial ARTINO radar experiments[C].8th European Conference Synthetic Aperture Radar 2010 (EUSAR2010),2010:1-4
[52]S. Jun, Z. Xiaoling, Y. Jianyu, et al.. APC trajectory design for "one-active" linear-array three-dimensional imaging SAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010,48(3):1470-1486
[53]S. J. Wei, X. L. Zhang, J. Shi, et al.. Sparse array microwave 3-D imaging:compressed sensing recovery and experimental study [J]. Progress In Electromagnetics Research,2013,135: 161-181.
[54]J. Shi, X. L. Zhang, J. Yang, et al.. Surface-tracing-based LASAR 3-D imaging method via multi-resolution approximation J]. IEEE Transactions on Geoscience and Remote Sensing,, 2008,46(11):3719-373Q.
[55]M. Cetin, C. K. William. Feature-enhanced synthetic aperture radar image formation based on nonquadratic regularization[J]. IEEE Trans on Image Processing,2001,10(4):623-631
[56]R. Baraniuk, P. Steeghs. Compressive radar imaging[C]. IEEE Radar Conference2007,2007: 128-133
[57]M. Herman, T. Strohmer. Compressed sensing radar[C]. IEEE Radar Conference 2008,2008: 1-6
[58]A. C. Gurbuz, J. H. McClellan, W. R. Scott. A compressive sensing data acquisition and imaging method for stepped frequency GPRs[J]. IEEE Transactions on Signal Processing,2009, 57(7):2640-2650
[59]J. Romberg. Imaging via compressive sampling[J]. IEEE Signal Processing,2008,25(2):14-20
[60]K. R. Varshney, M. Cetin, J. W. Fisher, et al.. Sparse representation in structured dictionaries with application to synthetic aperture radar[J]. IEEE Transactions on Signal Processing,2008, 56(8):3548-3561
[61]V. M. Patel, G. R. Easley, D. M. Healy, et al.. Compressed synthetic aperture radar[J]. IEEE Journal of Selected Topics in Signal Processing,2010,4(2):244-254
[62]A. C. Gurbuz, J. H. McClellan. Compressive sensing for subsurface imaging using ground penetrating radar[J]. Signal Processing,2009,89(10):1959-1972
[63]M. Ferrara, J. A. Jackson, C. Austin. Enhancement of multi-pass 3D circular SAR images using sparse reconstruction techniques[C]. SPIE Defense, Security, and Sensing. International Society for Optics and Photonics,2009:733702-733702-10
[64]X. X. Zhu, R. Bamler. Tomographic SAR inversion by LI norm regularization-the compressive sensing approach[J]. IEEE Transactions on Geoscience and Remote Sensing,2010,48(10): 3839-3846
[65]X. Tan, W. Roberts, J. Li, et al.. Sparse learning via iterative minimization with application to MIMO radar imaging[J]. IEEE Transactions on Signal Processing,2011,59(3):1088-1101
[66]A. C. Fannjiang, T. Strohmer, P. Yan. Compressed remote sensing of sparse objects[J]. SIAM Journal on Imaging Sciences,2010,3(3):595-618
[67]N. O. Onhon, M. Cetin. A sparsity-driven approach for joint SAR imaging and phase error correction[J]. IEEE Transactions on Image Processing,2012,21(4):2075-2088
[68]L. C. Potter, E. Ertin, J. T. Parker, et al.. Sparsity and compressed sensing in radar imaging[J]. Proceedings of the IEEE,2010,98(6):1006-1020
[69]J. K. Schindler. Sparse, active aperture imaging[J]. IEEE Journal of Selected Topics in Signal Processing,2010,4(1):202-209
[70]Y. Yu, A. P. Petropulu, H. V. Poor. MIMO radar using compressive sampling[J]. IEEE Journal of Selected Topics in Signal Processing,2010,4(1):146-163
[71]O. Batu, M. Cetin. Parameter selection in sparsity-driven SAR imaging[J]. IEEE Transactions on Aerospace and Electronic Systems,2011,47(4):3040-3050
[72]Y. Chi, L. L. Scharf, A. Pezeshki, et al.. Sensitivity to basis mismatch in compressed sensing[J]. IEEE Transactions on Signal Processing,2011,59(5):2182-2195
[73]S. Zhu, A. MohammadDjafari. A Bayesian approach to fourier synthesis inverse problem with application in SAR imaging[C]. AIP Conference Proceedings.2011,1305:258
[74]X. X. Zhu, R. Bamler. Super-resolution power and robustness of compressive sensing for spectral estimation with application to spaceborne tomographic SAR[J]. IEEE Transactions on Geoscience and Remote Sensing,2012,50(1):247-258
[75]S. Samadi, M. Cetin, M. A. Masnadi-Shirazi. Sparse representation-based synthetic aperture radar imaging[J]. IET Radar, Sonar & Navigation,2011,5(2):182-193
[76]M. A. C. Tuncer, A. C. Gurbuz. Ground reflection removal in compressive sensing ground penetrating radars[J]. IEEE Geoscience and Remote Sensing Letters,2012,9(1):23-27
[77]Y. Yu, A. P. Petropulu, H. V. Poor. CSSF MIMO RADAR:compressive-sensing and step-frequency based MIMO radar [J]. IEEE Transactions on Aerospace and Electronic Systems, 2012,48(2):1490-1504
[78]Q. Wu, M. Xing, C. Qiu, et al.. Motion parameter estimation in the SAR system with low PRF sampling[J]. IEEE Geoscience and Remote Sensing Letters,2010,7(3):450-454
[79]谢晓春,张云华.基于压缩感知的二维雷达成像算法[J].电子与信息学报,2010,32(5)：1234-1238
[80]Y. Zhang, J. Sun, J. Tian, et al.. Compressive sensing SAR imaging with real data[C]. IEEE 3rd International Congress on Image and Signal Processing (CISP2010),2010,4:2026-2029
[81]L. Zhang, M. Xing, C. W. Qiu, et al.. Resolution enhancement for inversed synthetic aperture radar imaging under low SNR via improved compressive sensing[J]. IEEE Transactions on Geoscience and Remote Sensing,2010,48(10):3824-3838
[82]Y. Lin, B. Zhang, W. Hong, et al.. MIMO SAR processing with azimuth nonuniform sampling[C]. IEEE International Geoscience and Remote Sensing Symposium 2010 (IGARSS2010),2010:4652-4655
[83]S. Wu, L. Zhang, M. Xing. Super-resolution ISAR imaging via statistical compressive sensing[C]. IEEE CIE International Conference on Radar 2011,2011,1:545-550.
[84]T. Jihua, S. Jinping, H.Xiao, et al.. Motion compensation for compressive sensing SAR imaging with autofocus[C].6th IEEE Conference on Industrial Electronics and Applications (ICIEA),2011:1564-1567
[85]X. Bai, F. Zhou, M. Xing, et al.. High-resolution radar imaging of air targets from sparse azimuth data[J]. IEEE Transactions on Aerospace and Electronic Systems,2012,48(2): 1643-1655
[86]L. Zhang, Z. Qiao, M. Xing, et al.. High-resolution ISAR imaging with sparse stepped-frequency waveforms [J]. IEEE Transactions on Geoscience and Remote Sensing,2011, 49(11):4630-4651.
[87]江海,林月冠,张冰尘,洪文.基于压缩感知的随机噪声成像雷达[J].电子与信息学报,2011(05)
[88]徐刚,包敏,李亚超,等.基于贝叶斯估计的高精度ISAR成像[J].系统工程与电子技术,2011,33(11)：2382-2388
[89]徐建平,皮亦鸣,曹宗杰.基于贝叶斯压缩感知的合成孔径雷达高分辨成像[J].电子与信息学报,2011,33(12)：2864-2868.
[90]顾福飞,池龙,张群,等.基于压缩感知的稀疏阵列MIMO雷达成像方法[J1.电子与信息学报,2011,33(10)：2452-2457.
[91]C. L. Jiang, B. C. Zhang, Z. Zhang, et al.. Experimental results and analysis of sparse microwave imaging from spaceborne radar raw data[J]. Science China Information Sciences, 2012,55(8):1801-1815
[92]H. P. Xu, Y. N. You, C. S. Li, et al.. Spotlight SAR sparse sampling and imaging method based on compressive sensing[J]. Science China Information Sciences,2012,55(8):1816-1829
[93]B. C. Zhang, W. Hong, Y. R. Wu. Sparse microwave imaging:principles and applications[J]. Science China Information Sciences,2012,55(8):1722-1754
[94]J. Shi, X. L. Zhang, G Xiang, et al.. Signal processing for microwave array imaging:TDC and sparse recovery[J]. IEEE Transactions on Geoscience and Remote Sensing,2012. 50(11):4584-4598
[95]G. Xu, J. L. Sheng, L. Zhang, et al.. Performance improvement in multi-ship imaging for ScanSAR based on sparse representation[J]. Science China Information Sciences,2012,55(8): 1860-1875
[96]陈原,张荣,尹东.基于Tetrolet Packet变换的SAR图像稀疏表示[J].电子与信息学报,2012,34(2)：261-267
[97]G. M. Shi, C. Y. Chen, X. Y. Chen, J. Lin. Narrowband ultrasonic detection with high range resolution:separating echoes via compressed sensing and singular value decomposition [J]. IEEE Transactions on Ultrasonics,Ferroelectrics, and Frequency Control,2012,59(10): 2237-2253
[98]全英汇.稀疏信号处理在雷达检测和成像中的应用研究[D].西安电子科技大学,2012
[99]薛会祥.基于压缩感知理论的DOA估计算法研究[D].解放军信息工程大学,2012
[100]黄琼,屈乐乐,吴秉横,等.压缩感知在超宽带雷达成像中的应用[J].电波科学学报,2010,25(1)：77-82
[101]王文超.压缩感知理论在探地雷达成像中的应用研究[D].华东交通大学,2012
[102]徐浩,尹治平,刘畅畅,等.基于压缩感知的稀疏无源雷达成像[J].系统工程与电子技术,2011,33(12)：2623-2630
[103]R. John, Higgins. Sampling theory in Fourier and signal analysis:foundations[M]. Oxford University Press,1996
[104]D. L. Donoho. Compressed sensing[J]. IEEE Transactions on Information Theory,2006,52(4): 1289-1306
[105]R. G. Baraniuk. Compressive sensing [J]. IEEE Signal Processing,2007,24(4):118-121
[106]Y. Tsaig, D. L. Donoho. Extensions of compressed sensing[J]. Signal processing,2006,86(3): 549-571
[107]E. J. Candes, M. B. Wakin. An introduction to compressive sampling[J]. IEEE Signal Processing Magazine,2008,25(2):21-30
[108]E. J. Candes. The restricted isometry property and its implications for compressed sensing[J]. Comptes Rendus Mathematique,2008,346(9):589-592
[109]H. Rauhut, K. Schnass, P. Vandergheynst. Compressed sensing and redundant dictionaries [J]. IEEE Transactions on Information Theory,2008,54(5):2210-2219
[110]E. J. Candes, Y. C. Eldar, D. Needell, et al.. Compressed sensing with coherent and redundant dictionaries[J]. Applied and Computational Harmonic Analysis,2011,31(1):59-73
[111]R. J. Duffin, A. C. Schaeffer. A class of non-harmonic Fourier series[J]. Transactions of the American Mathematical Society,1952,72(2):341-366
[112]M. Elad, M. Aharon. Image denoising via sparse and redundant representations over leameddictionaries[J]. IEEE Transactions on Image Processing,2006,15(12):3736-3745
[113]M. Elad, M. Aharon,A. Bruckstein. K-SVD:an algorithm for designing over-completesdictionaries for sparse representation[J].IEEE Transactions on Signal Processing,2006,54(11):4311-4322
[114]R. Mazhar, P. D. Gader. EK-SVD:optimized dictionary design for sparse representations[C]. 19th International Conference on Pattern Recognition, Tampa, Florida,2008,11:1-4
[115]Q. Zhang, B. X. Li. Discriminative K-SVD for dictionary learning in face recognition[C]. IEEE Conference on Computer Vision and Pattern Recognition, San Francisco,2010, 7:2691-2698
[116]E. J. Candes, T. Tao. Near-optimal signal recovery from random projections:universal encoding strategies [J]. IEEE Transactions on Information Theory,2006,52(12):5406-5425
[117]E. J. Candes, J. K. Romberg, T. Tao. Stable signal recovery from incomplete and inaccurate measurements[J]. Communications on Pure and Applied Mathematics,2006,59(8):1207-1223
[118]E. J. Candes. The restricted isometry property and its implications for compressed sensing[J]. Comptes Rendus Mathematique,2008,346(9):589-592
[119]S. G. Mallat, Zhang Z. Matching pursuits with time-frequency dictionaries [J]. IEEE Transactions on Signal Processing,1993,41(12):3397-3415
[120]J. A. Tropp, A. C. Gilbert. Signal recovery from random measurements via orthogonal matching pursuit[J]. IEEE Transactions on Information Theory,2007,53(12):4655-4666
[121]D. L. Donoho, Y. Tsaig, I. Drori, et al.. Sparse solution of underdetermined systems of linear equations by stagewise orthogonal matching pursuit[J]. IEEE Transactions on Information Theory,2012,58(2):1094-1121
[122]D. Needell, R. Vershynin. Uniform uncertainty principle and signal recovery via regularized orthogonal matching pursuit[J]. Foundations of Computational Mathematics,2009,9(3): 317-334
[123]D. Needell, J. A. Tropp. CoSaMP:Iterative signal recovery from incomplete and inaccurate samples[J]. Applied and Computational Harmonic Analysis,2009,26(3):301-321
[124]S. S. Chen, D. L. Donoho, Saunders M A. Atomic decomposition by basis pursuit[J], SIAM journal on scientific computing,1998,20(1):33-61
[125]R. Tibshirani. Regression shrinkage and selection via the lasso[J]. Journal of the Royal Statistical Society. Series B (Methodological),1996:267-288
[126]R. Chartrand, W. Yin. Iteratively reweighted algorithms for compressive sensing[C]. IEEE International Conference on Acoustics, Speech and Signal Processing 2008(ICASSP 2008). 2008:3869-3872
[127]M. A. T. Figueiredo, R. D. Nowak, S. J. Wright. Gradient projection for sparse reconstruction: application to compressed sensing and other inverse problems[J]. IEEE Journal of Selected Topics in Signal Processing,2007,1(4):586-597
[128]T. Blumensath, M. E. Davies. Iterative hard thresholding for compressed sensing[J]. Applied and Computational Harmonic Analysis,2009,27(3):265-274
[129]K. Bredies, D. A. Lorenz. Linear convergence of iterative soft-thresholding[J]. Journal of Fourier Analysis and Applications,2008,14(5-6):813-837
[130]S. Yu, R. Wang, W. Wan, et al.. Compressed sensing in audio signals and it's reconstruction algorithm[C]. IEEE International Conference on Audio, Language and Image Processing 2012(ICALIP2012),2012:947-952
[131]P. Gong, J. Zhou, Z. Shao, et al..A near-field imaging algorithm based on SIMO-SAR system[C]. IEEE International Conference on Computational Problem-Solving 2011 (ICCP2011),2011:678-681
[132]J. H. G Ender, J. Klare. System architectures and algorithms for radar imaging by MIMO-SAR[C]. IEEE Radar Conference 2009,2009:1-6
[133]S. Repetto, M. Palmese, A. Trucco. High-resolution 3-D imaging by a sparse array:array optimization and image simulation[C]. IEEE Europe Oceans 2005,2005,2:763-768
[134]J. Shi, X. L. Zhang, J, Y. Yang, et al.. APC trajectory design for "one-active" linear-array three-dimensional imaging SAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010,48(3):1470-1486
[135]L. Van Hove. Correlations in space and time and Born approximation scattering in systems of interacting particles [J]. Physical Review,1954,95(1):249
[136]V. C. Chen, H. Ling. Time-frequency transforms for radar imaging and signal analysis[M]. Artech house,2002
[137]R. Bamler. A comparison of range-Doppler and wavenumber domain SAR focusing algorithms[J]. IEEE Transactions on Geoscience and Remote Sensing,1992,30(4):706-713
[138]J. M. Lopez-Sanchez, J. Fortuny-Guasch.3-D radar imaging using range migration techniques[J]. IEEE Transactions on Antennas and Propagation,2000,48(5):728-737
[139]A. Ishimaru, T. K. Chan, Y. Kuga. An imaging technique using confocal circular synthetic aperture radar[J]. IEEE Transactions on Geoscience and Remote Sensing,1998,36(5): 1524-1530
[140]J. Shi, X. L. Zhang, J. Y. Yang, et al.. Surface-tracing-based LASAR 3-D imaging method via multiresolution approximation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008,46(11):3719-3730
[141]A. Moreira, Y. Huang. Airborne SAR processing of highly squinted data using a chirp scaling approach with integrated motion compensation[J]. IEEE Transactions on Geoscience and Remote Sensing,1994,32(5):1029-1040
[142]G. San Antonio, D. R. Fuhrmann, F. C. Robey. MIMO radar ambiguity functions[J]. IEEE Journal of Selected Topics in Signal Processing,,2007,1(1):167-177
[143]帅君.双基地SAR与线阵SAR原理及成像技术研究[D],电子科技大学,2009
[144]C. D. Austin, E. Ertin, R. L. Moses. Sparse signal methods for 3-D radar imaging[J]. IEEE Journal of Selected Topics in Signal Processing,2011,5(3):408-423
[145]M. Mishali, Y. C. Eldar. Blind multiband signal reconstruction:compressed sensing for analog signals[J]. IEEE Transactions on Signal Processing,2009,57(3):993-1009
[146]Y. S. Yoon, M. G. Amin. Compressed sensing technique for high-resolution radar imaging[C]. SPIE Defense and Security Symposium. International Society for Optics and Photonics,2008: 69681A-69681A-10
[147]I. Stojanovic, W. C. Karl, M. Cetin. Compressed sensing of mono-static and multi-static SAR[C]. SPIE Defense, Security, and Sensing. International Society for Optics and Photonics, 2009:733705-733705-12
[148]S. J. Wei, X. L. Zhang, J. Shi, et al.. Sparse reconstruction for SAR imaging based on compressed sensing[J]. Progress In Electromagnetics Research,2010,109:63-81
[149]C. D. Austin, E. Ertin, R. L. Moses. Sparse multipass 3D SAR imaging:applications to the GOTCHA data set[C]. SPIE Defense, Security, and Sensing. International Society for Optics and Photonics,2009:733703-733703-12
[150]L. Zhang, M. Xing, C. W. Qiu, et al.. Resolution enhancement for inversed synthetic aperture radar imaging under low SNR via improved compressive sensing[J]. IEEE Trans on Geosci Remote Sens,2010.48,10:3824-3838
[151]X. C. Xie, Y. H. Zhang. High-resolution imaging of moving train by ground-based radar with compressive sensing. Electron Lett,2010,46:529-531
[152]M. Cetin, O. Onhon, S. Samadi. Handling phase in sparse reconstruction for SAR:Imaging, autofocusing, and moving targets[C].9th European Conference on Synthetic Aperture Radar 2012(EUSAR2012), VDE,2012:207-210
[153]W. Min. High resolution radar imaging based on compressed sensing and adaptive Lp norm algorithm[C]. IEEE CIE International Conference on Radar 2011,2011,1:206-209
[154]M. Wang, S. Yang, Y. Wan, et al.. High resolution radar imaging based on compressed sensing and fast Bayesian matching pursuit[C]. IEEE International Workshop on Multi-Platform/Multi-Sensor Remote Sensing and Mapping 2011,2011:1-5
[155]R. T. Lord, M. R. Inggs. High resolution SAR processing using stepped-frequencies[C]. IEEE International Geoscience and Remote Sensing 1997(IGARSS'97),1997,1:490-492
[156]M. A. Davenport, M. B. Wakin. Analysis of orthogonal matching pursuit using the restricted isometry property[J]. IEEE Transactions on Information Theory,2010,56(9):4395-4401
[157]S. Ji, Y. Xue, L. Carin. Bayesian compressive sensing[J]. IEEE Transactions on Signal Processing,2008,56(6):2346-2356
[158]F. Dell'Acqua, P. Gamba. Texture-based characterization of urban environments on satellite SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing,2003,41(1):153-159
[159]S.A.S. Wemess, W. G. Carrara, L. S. Joyce, et al.. Moving target imaging algorithm for SAR data[J]. IEEE Transactions on Aerospace and Electronic Systems,1990,26(1):57-67
[160]J. A. Tropp. Greed is good:Algorithmic results for sparse approximation[J]. IEEE Transactions on Information Theory,2004,50(10):2231-2242
[161]T. T. Cai, L. Wang. Orthogonal matching pursuit for sparse signal recovery with noise[J]. IEEE Transactions on Information Theory,2011,57(7):4680-4688
[162]R. Fan, Q. Wan, Y. Liu, et al.. Complex orthogonal matching pursuit and its exact recovery conditions [J]. arXiv preprint arXiv:1206.2197,2012
[163]D. Needell, R. Vershynin. Signal recovery from incomplete and inaccurate measurements via regularized orthogonal matching pursuit[J]. IEEE Journal of Selected Topics in Signal Processing,2010,4(2):310-316
[164]R. Giryes, M. Elad. RIP-based near-oracle performance guarantees for SP, CoSaMP, and IHT[J]. IEEE Transactions on Signal Processing,2012,60(3):1465-1468
[165]T. D. Ross, S. W. Worrell, V. J. Velten, et al.. Standard SAR ATR evaluation experiments using the MSTAR public release data set[C]. International Society for Optics and Photonics Aerospace/Defense Sensing and Controls,1998:566-573
[166]廖可非,单激励三维SAR实验系统及成像技术研究[D],电子科技大学,2010
[167]K. F. Liao, X. L. Zhang, J. Shi, et al. Three-dimensional microwave imaging method via subaperture approximation[C]. IEEE International Geoscience and Remote Sensing Symposium (IGARSS2011),2011:3704-3707
[168]J. Shi, X. L. Zhang, J. Y. Yang, et al.. Experiment data processing on" one-active" LASAR[C]. IET International Radar Conference 2009, Guillin,2009,1-4
[169]D. P. Wipf, B. D. Rao. Sparse Bayesian learning for basis selection[J]. IEEE Transactions on Signal Processing,2004,52(8):2153-2164
[170]R. A. Redner, H. F. Walker. Mixture densities, maximum likelihood and the EM algorithm[J]. SIAM review,1984,26(2):195-239
[171]M. E. Tipping. Sparse Bayesian learning and the relevance vector machine[J]. The Journal of Machine Learning Research,2001,1:211-244
[172]G. Oliveri, P. Rocca, A. Massa. A Bayesian-compressive-sampling-based inversion for imaging sparse scatterers[J]. IEEE Transactions on Geoscience and Remote Sensing,2011,49(10): 3993-4006
[173]T. Yardibi, J. Li, P. Stoica, et al.. Source localization and sensing:a nonparametric iterative adaptive approach based on weighted least squares[J]. IEEE Transactions on Aerospace and Electronic Systems,2010,46(1):425-443
[174]S. D. Babacan, R. Molina, A. K. Katsaggelos. Bayesian compressive sensing using Laplace priors[J]. IEEE Transactions on Image Processing,2010,19(1):53-63
[175]G. H. Golub, P. C. Hansen, D. P. O'Leary. Tikhonov regularization and total least squares[J]. SIAM Journal on Matrix Analysis and Applications,1999,21(1):185-194
[176]Y. Tamura, T. Sato, M. Ooe, et al.. A procedure for tidal analysis with a Bayesian information criterion[J]. Geophysical Journal International,1991,104(3):507-516
[177]D. Posada, T. R. Buckley. Model selection and model averaging in phylogenetics:advantages of Akaike information criterion and Bayesian approaches over likelihood ratio tests[J]. Systematic biology,2004,53(5):793-808
[178]C. Y. Sin, H. White. Information criteria for selecting possibly misspecified parametric models[J]. Journal of Econometrics,1996,71(1):207-225
[179]W. Roberts, P. Stoica, J. Li, et al.. Iterative adaptive approaches to MIMO radar imaging[J]. IEEE Journal of Selected Topics in Signal Processing,2010,4(1):5-20
[180]B. Efron, C. Morris. Data analysis using Stein's estimator and its generalizations[J]. Journal of the American Statistical Association,1975,70(350):311-319
[181]G. H. Golub, M. Heat, G. Wahba. Generalized cross-validation as a method for choosing a good ridge parameter[J]. Technometrics,1979,21(2):215-223
[182]M. R. Hestenes, E. Stiefel. Methods of conjugate gradients for solving linear systems[J]. Journal of Research of the National Nureau of Standard,1952.49(6):409463
[183]Y. L. Chang, X. Li. Adaptive image region-growing[J]. IEEE Transactions on Image Processing,1994,3(6):868-872
[184]皮亦鸣,杨建宇,付毓生,杨晓波.合成孔径雷达成像原理[M].电子科技大学出版社,2007
[185]H. Xie, L. E. Pierce, F. T. Ulaby. SAR speckle reduction using wavelet denoising and Markov random field modeling[J]. IEEE Transactions on Geoscience and Remote Sensing,2002, 40(10):2196-2212
[186]D. E. Wahl, P. H. Eichel, D. C. Ghiglia, et al.. Phase gradient autofocus-a robust tool for high resolution SAR phase correction[J]. IEEE Transactions on Aerospace and Electronic Systems, 1994,30(3):827-835
[187]J. Wang, X. Liu. SAR minimum-entropy autofocus using an adaptive-order polynomial model[J]. IEEE Geoscience and Remote Sensing Letters,2006,3(4):512-516
[188]J. R. Fienup. Synthetic-aperture radar autofocus by maximizing sharpness[J]. Optics Letters, 2000,25(4):221-223
[189]R. L. Morrison, M. N. Do, D. C. Munson. MCA:a multichannel approach to SAR autofocus[J]. IEEE Transactions on Image Processing,2009,18(4):840-853
[190]W. Ye, T. S. Yeo, Z. Bao. Weighted least-squares estimation of phase errors for SAR/ISAR autofocus[J]. IEEE Transactions on Geoscience and Remote Sensing,1999,37(5):2487-2494
[191]C. V. JakowatzJr, D. E. Wahl. Eigenvector method for maximum-likelihood estimation of phase errors in synthetic-aperture-radar imagery[J]. JOSAA,1993,10(12):2539-2546
[192]F. Berizzi, G. Corsini, M. Diani, et al.. Autofocus of wide azimuth angle SAR images by contrast optimisation[C]. IEEE International Geoscience and Remote Sensing Symposium, 1996(IGARSS1996),1996,2:1230-1232
[193]T. J. Schulz. Optimal sharpness function for SAR autofocus[J]. IEEE Signal Processing Letters, 2007,14(1):27-30
[194]J. Wang, X. Liu, Z. Zhou. Minimum-entropy phase adjustment for ISAR[J]. IET Radar, Sonar and Navigation,2004,151(4):203-209
[195]L. Vandenberghe, S. Boyd. Semi-definite programming[J]. SIAM review,1996,38(1):49-95
[196]K. Anstreicher, H. Wolkowicz. On Lagrangian relaxation of quadratic matrix constraints[J]. SIAM Journal on Matrix Analysis and Applications,2000,22(1):41-55
[197]Z. Luo, W. Ma, A. M. C. So, et al..Semidefinite relaxation of quadratic optimization problems[J]. IEEE Signal Processing Magazine,2010,27(3):20-34
[198]T. M. Calloway, G. W. Donohoe. Subaperture autofocus for synthetic aperture radar[J]. IEEE Transactions on Aerospace and Electronic Systems,1994,30(2):617-621
[199]武昕伟.SAR自聚焦技术及相干斑抑制算法研究[D].南京航空航天大学,2002
[200]C. V. JakowatzJr, D. E. Wahl. Eigenvector method for maximum-likelihood estimation of phase errors in synthetic-aperture-radar imagery[J]. JOSA,1993,10(12):2539-2546
[201]R. L. Morrison, M. N. Do. Multichannel autofocus algorithm for synthetic aperture radar[C]. IEEE International Conference on Image Processing 2006,2006:2341-2344
[202]Y. Nesterov. Semidefinite relaxation and nonconvex quadratic optimization[J]. Optimization methods and software,1998,9(1-3):141-160
[203]K. H. Liu, A. Wiesel, D. C. Munson. Synthetic aperture radar autofocus via semidefiniterelaxation[C]. IEEE International Conference on Acoustics Speech and Signal Processing2010 (ICASSP2010),2010:1342-1345
[204]K. H. Liu, A. Wiesel, D. C. Munson. Synthetic aperture radar autofocus based on a bilinear model[J]. IEEE Transactions on Image Processing,2012,21(5):2735-2746
[205]J. B. C. Silva, G. W. Hohmann. Nonlinear magnetic inversion using a random search method[J]. Geophysics,1983,48(12):1645-1658
[206]M. Skolnik, J. Sherman. Statistically designed density-tapered arrays[J], IEEE Trans on Antennas Propagation,1964,12(7):408-417