SAR层析三维成像技术研究
|
摘要
合成孔径雷达(SAR)三维成像,既具有传统SAR全天时、全天候、高分辨率成像及电磁穿透等优势,又避免了二维成像时三维场景空间投影到二维成像平面时的模糊问题,在军事侦察、地球遥感、海洋研究、环境保护以及灾情监测等方面具有广泛的应用前景,正逐渐成为雷达技术领域的研究热点之一。SAR层析三维成像技术,利用不同入射角轨迹上获得的多个二维SAR成像结果,基于层析原理在二维成像平面的法线方向上进行孔径合成,从而实现高分辨三维成像。该技术既不需要复杂的飞行轨迹控制、额外的定位精度要求,又拥有真正的三维成像能力,在雷达成像领域具有很高的研究价值。然而,SAR层析三维成像技术还不完善,在理论、系统和应用等方面都面临着问题与挑战。
本文以SAR层析三维成像技术为研究对象,重点针对系统理论、图像配准、噪声抑制、稀疏轨迹、机载平台拓展等关键技术开展了较为系统、深入的研究,完成的主要工作和贡献如下:
1)完善了SAR层析三维成像理论与系统。本文以层析三维成像的视角分析了雷达信号的层析成像特征,详细地阐述了SAR三维层析成像理论,推导了SAR层析三维成像的性能。分析了SAR层析三维成像系统中二维成像分辨率、轨迹定位误差、图像配准精度、轨迹数量及分布等系统因素对SAR层析三维成像的影响。研究了SAR层析三维成像系统中图像配准、成像等关键问题的解决方法。
2)提出一种基于区域特征的多SAR图像配准算法。该算法通过水平集函数与配准函数的同步迭代求解泛函极值获得图像的配准函数实现图像配准。该算法抑制了SAR图像相干斑对区域特征识别的影响,并有效避免了特征识别误差向配准过程的传播。
3)针对SAR层析三维成像中复杂的噪声环境,提出了一种适应复杂噪声环境的SAR层析三维成像算法。该算法基于高阶统计理论,将成像建模为噪声中的谐波恢复模型,提高了SAR层析三维成像系统对图像间去相关因素及环境的适应性。
4)提出了一种稀疏轨迹SAR层析三维成像算法。该算法基于多散射中心假设,将成像问题建模为信号稀疏表示问题,再利用稀疏贝叶斯学习算法求解实现成像。降低了SAR层析三维成像对轨迹的要求,提高了SAR层析三维成像的适用性。
5)初步设计了一种多天线机载SAR层析三维成像系统。该系统采用单发多收的组合模式,降低了SAR层析三维成像对机载平台的导航精度和飞行密度的要求,降低了飞行风险,拓展了SAR层析三维成像在机载平台的应用。
本文重点研究了SAR层析三维成像实际应用中的几个问题,丰富了SAR层析三维成像系统和理论。随着SAR层析三维成像研究的逐渐深入,应用条件的逐渐成熟,SAR层析三维成像技术必将得到广泛的应用。
In recent years, the technique of three-dimensional Synthetic Aperture Radar (SAR) imaging has been becoming one of the focuses in RADAR field because of such specific advantages as all-time, all-weather, high-resolution, electromagnetic penetration, no projection blurring and broad application prospects in military reconnaissance, earth remote sensing, marine research, environmental protection and disaster monitoring. Based on the tomography, SAR tomography (TomoSAR) can generate high resolution three-dimensional imagery by combining multiple SAR images acquired on flight paths slightly separated in the elevation direction. In the field of radar imaging, TomoSAR has important research value because it have a true three-dimensional imaging capability, does not require complex flight trajectory controls, additional positioning accuracy requirements. However, many problems and challenges are needed to be resolved for perfecting the TomoSAR.
This dissertation carries out research on TomoSAR with focuses on the key techniques such as systems theory, SAR images co-registration, suppressing noise interference, imaging with sparse baselines and airborne extended application. The main work and contributions are presented as follows:
1) To some extent, TomoSAR theory and system are supplemented. With the view of tomography, the characters of radar signal are analyzed firstly. Secondly, the TomoSAR three-dimensional imaging principle is presented and the performances of TomoSAR are deduced. Then the effects from the factors of system are discussed such as the resolution of SAR images, the errors in track positioning, the precision of SAR image co-registration, noise, the number and distribution of tracks et al. Fourthly, the solutions to some key problems are studied and presented.
2) A novel feature-based method for SAR images co-registration is proposed. In this method, the co-registration is formulated as a functional optimization problem based on level set firstly, then the level set function and the registration function are interleaving evolved iteratively to register the images. By this method, the speckle effect on feature recognition is suppressed and the errors occurred in recognition is avoided to impact registration simultaneously.
3) A new imaging approach is proposed for TomoSAR based on higher-order statistics. By formulating the imaging to harmonic retrieval problems based on higher-order cummulants, this approach can deal with colored (and whiten) Gaussian noise and symmetric (and non-symmetric) non-Gaussian noise. The TomoSAR is extended to more complex situations.
4) To deal with TomoSAR imaging with sparse baselines, an approach is proposed based on sparse signal representation. Firstly, the imaging is formulated as a signal sparse representation problem through the hypothesis that the signal in elevation direction are from limited scattering centers. Then the imagery is obtained by solving the representation problem through sparse Bayesian learning algorithm. With this approach, the TomoSAR can obtain imagery from more flexible SAR tracks both number and distribution.
5) An airborne TomoSAR system configuration with multi-antennas is presented where all the antennas are distributed along the wings, one antenna transmits and receives electromagnetic waves, and the others are receivers only. With this configuration, the TomoSAR can work with fewer flights, less flight risk, larger flight positioning precision.
In this dissertation, some practical issues of three-dimensional SAR tomography imaging are mainly discussed. To a certain extent, the work presented in this dissertation enriches the TomoSAR systems and theories. With the devolopement of TomoSAR, it will be applied in various fields.
本文以SAR层析三维成像技术为研究对象,重点针对系统理论、图像配准、噪声抑制、稀疏轨迹、机载平台拓展等关键技术开展了较为系统、深入的研究,完成的主要工作和贡献如下:
1)完善了SAR层析三维成像理论与系统。本文以层析三维成像的视角分析了雷达信号的层析成像特征,详细地阐述了SAR三维层析成像理论,推导了SAR层析三维成像的性能。分析了SAR层析三维成像系统中二维成像分辨率、轨迹定位误差、图像配准精度、轨迹数量及分布等系统因素对SAR层析三维成像的影响。研究了SAR层析三维成像系统中图像配准、成像等关键问题的解决方法。
2)提出一种基于区域特征的多SAR图像配准算法。该算法通过水平集函数与配准函数的同步迭代求解泛函极值获得图像的配准函数实现图像配准。该算法抑制了SAR图像相干斑对区域特征识别的影响,并有效避免了特征识别误差向配准过程的传播。
3)针对SAR层析三维成像中复杂的噪声环境,提出了一种适应复杂噪声环境的SAR层析三维成像算法。该算法基于高阶统计理论,将成像建模为噪声中的谐波恢复模型,提高了SAR层析三维成像系统对图像间去相关因素及环境的适应性。
4)提出了一种稀疏轨迹SAR层析三维成像算法。该算法基于多散射中心假设,将成像问题建模为信号稀疏表示问题,再利用稀疏贝叶斯学习算法求解实现成像。降低了SAR层析三维成像对轨迹的要求,提高了SAR层析三维成像的适用性。
5)初步设计了一种多天线机载SAR层析三维成像系统。该系统采用单发多收的组合模式,降低了SAR层析三维成像对机载平台的导航精度和飞行密度的要求,降低了飞行风险,拓展了SAR层析三维成像在机载平台的应用。
本文重点研究了SAR层析三维成像实际应用中的几个问题,丰富了SAR层析三维成像系统和理论。随着SAR层析三维成像研究的逐渐深入,应用条件的逐渐成熟,SAR层析三维成像技术必将得到广泛的应用。
In recent years, the technique of three-dimensional Synthetic Aperture Radar (SAR) imaging has been becoming one of the focuses in RADAR field because of such specific advantages as all-time, all-weather, high-resolution, electromagnetic penetration, no projection blurring and broad application prospects in military reconnaissance, earth remote sensing, marine research, environmental protection and disaster monitoring. Based on the tomography, SAR tomography (TomoSAR) can generate high resolution three-dimensional imagery by combining multiple SAR images acquired on flight paths slightly separated in the elevation direction. In the field of radar imaging, TomoSAR has important research value because it have a true three-dimensional imaging capability, does not require complex flight trajectory controls, additional positioning accuracy requirements. However, many problems and challenges are needed to be resolved for perfecting the TomoSAR.
This dissertation carries out research on TomoSAR with focuses on the key techniques such as systems theory, SAR images co-registration, suppressing noise interference, imaging with sparse baselines and airborne extended application. The main work and contributions are presented as follows:
1) To some extent, TomoSAR theory and system are supplemented. With the view of tomography, the characters of radar signal are analyzed firstly. Secondly, the TomoSAR three-dimensional imaging principle is presented and the performances of TomoSAR are deduced. Then the effects from the factors of system are discussed such as the resolution of SAR images, the errors in track positioning, the precision of SAR image co-registration, noise, the number and distribution of tracks et al. Fourthly, the solutions to some key problems are studied and presented.
2) A novel feature-based method for SAR images co-registration is proposed. In this method, the co-registration is formulated as a functional optimization problem based on level set firstly, then the level set function and the registration function are interleaving evolved iteratively to register the images. By this method, the speckle effect on feature recognition is suppressed and the errors occurred in recognition is avoided to impact registration simultaneously.
3) A new imaging approach is proposed for TomoSAR based on higher-order statistics. By formulating the imaging to harmonic retrieval problems based on higher-order cummulants, this approach can deal with colored (and whiten) Gaussian noise and symmetric (and non-symmetric) non-Gaussian noise. The TomoSAR is extended to more complex situations.
4) To deal with TomoSAR imaging with sparse baselines, an approach is proposed based on sparse signal representation. Firstly, the imaging is formulated as a signal sparse representation problem through the hypothesis that the signal in elevation direction are from limited scattering centers. Then the imagery is obtained by solving the representation problem through sparse Bayesian learning algorithm. With this approach, the TomoSAR can obtain imagery from more flexible SAR tracks both number and distribution.
5) An airborne TomoSAR system configuration with multi-antennas is presented where all the antennas are distributed along the wings, one antenna transmits and receives electromagnetic waves, and the others are receivers only. With this configuration, the TomoSAR can work with fewer flights, less flight risk, larger flight positioning precision.
In this dissertation, some practical issues of three-dimensional SAR tomography imaging are mainly discussed. To a certain extent, the work presented in this dissertation enriches the TomoSAR systems and theories. With the devolopement of TomoSAR, it will be applied in various fields.
引文
[1] Soumekh M. Synthetic aperture radar signal processing with MATLAB algorithms. New York: John Wiley & Sons, 1999, 1-259.
[2] Curlander J C, McDonough R N. Synthetic aperture radar: systems and signal processing. New York: John Wiley & Sons, 1991, 1-225.
[3] Hein A. Processing of SAR data: fundamentals, signal processing, interferometry. Berlin: Springe, 2004, 1-280.
[4]皮亦鸣,杨建宇.合成孔径雷达成像原理.成都:电子科技大学出版社, 2007, 1-155.
[5]张直中.机载和星载合成孔径雷达导论.北京:电子工业出版社, 2004, 1-200.
[6] Cumming I G.合成孔径雷达成像—算法与实现(洪文,胡东辉,译).北京:电子工业出版社, 2007, 1-283.
[7]刘永坦.雷达成像技术.哈尔滨:哈尔滨工业大学出版社, 1999, 1-116.
[8]保铮,邢孟道,王彤.雷达成像技术.北京:电子工业出版社, 2005, 1-315.
[9]邢孟道,王彤.雷达信号处理基础.北京:电子工业出版社, 2008, 288-339.
[10] Maitre H.合成孔径雷达图像处理(孙洪译).北京:电子工业出版社, 2005, .1-103.
[11] Rogers A E, Ingalls R P. Venus: mapping the surface reflectivity by radar interferometry. Science, 1969, 165(3895): 797-799.
[12] Zebker H A, Goldstein R M. Topographic mapping from interferometric synthetic aperture radar observations. Journal of Geophysical Research, 91(B5): 4993-5000.
[13] Goldstein R M, Zebker H A, Werner C L. Satellite radar interferometry: two-dimensional phase unwrapping. Radio Science, 1988, 223(4): 713-720.
[14] Gabriel A K, Goldstein R M. Crossed orbit interferometry: theory and experiment results from SIRB. Int. J. Remote Sens., 1988, 9(5):857-872.
[15] Li F, Goldstein R M. Studies of multibaseline spaceborne interferometric synthetic aperture radars. IEEE Trans. Geosci. Remote Sens. 1990, 28(1): 88-97.
[16] Rodrigurez E, Martin J M. Theory and design of interferometric synthetic aperture radar. IEE Proc. Radar and Signal Processing, 1992, 139(2):147-159.
[17] Zebker H A, Cillasenor J. Decorrelation in interferometric radar echoes. IEEE Trans. Geosci. Remote Sens., 1992, 30(5): 950-959.
[18] Lin Q, Vesecky J, Zebker H. New approaches in interferometric SAR data processing. IEEE Trans. Geosci. Remote Sens., 1992, 30(3): 560-567.
[19] Bamler R, Hartl P. Synthetic aperture radar interferometry. Inverse Problems, 1998, 14(4): R1-R54.
[20] Rosen P A, Hensley S, Joughin I R, et al. Synthetic aperture radar interferometry. IEEE Proceedings, 2000, 88(3): 333-382.
[21]王超,张红,刘智.星载合成孔径雷达干涉测量.北京:科学出版社, 2002: 1-156.
[22] Knaell K. Three-dimensional SAR from practical apertures. Proceedings of SPIE, Sna Diego, CA, USA, 1995:31-41.
[23] Knaell K. Three-dimensional SAR from curvilinear apertures. IEEE Radar Conference (RADAR), Ann Arbor, Michigan, USA, 1996:220-225.
[24] Knaell K. Advances in three-dimensional SAR from curvilinear apertures. Proceedings of SPIE, San Diego, CA, USA, 1997: 178-184.
[25] Knaell K. Progress in three-dimensional SAR from curvilinear apertures. Proceedings of SPIE, San Diego, CA, USA, 1998: 110-121.
[26] Li J, Bi Z, Liu Z. Autofocus and feature extraction in curvilinear SAR via a relaxation-based algorithm. Proceedings of SPIE, Orlando, Fl, USA, 1998: 390-401.
[27] Su Z, Wu R, Liu J, et al. A robust sutofocus algorithm for the 3-D terget feature extraction in curvilinear SAR. CIE Int. Conf.Radar Proc., Beijing, China, 2001: 644-648.
[28] Su Z, Peng Y, Wang X. Evaluation of the aperture in the curvilinear SAR. CIE Int. Conf.Radar Proc., Shanghai, China, 2006, CDROM.
[29] Knaell K K, Cardillo G P. Radar tomography for the generation of three-dimensional images. IEE Eng. Radar Sonar Navig., 1995, 142(2): 54-60.
[30] Pasquali P, Prati C, Rocca F, et al. A 3D SAR experiment with EMSL data. International Geoscience and Remote Sensing Symposium (IGARSS), Firenze, Italy, 1995: 784–786.
[31] Reigber A, Moreira A, Papathanassiou K P. First demonstration of airborne SAR tomography using multibaseline L-band data. International Geoscience and Remote Sensing Symposium (IGARSS), Hamburg, Germany, 1999:44-46.
[32] Reigber A, Moreira A. First demonstration of airborne SAR tomography using multibaseline L-band data. IEEE Trans. Geosci. Remote Sens., 2000, 38(9): 2142-2152.
[33] She Z, Gray D A, Bogner R E, et al. Three-dimensional spaceborne synthetic aperture radar(SAR) imaging with multipass processing. Int. J. Remote Sens., 23(4):4357-4382.
[34] Homer J, Longstaff I D, She Z, et al. High resolution 3-D imaging via multi-pass SAR. IEE Proc. Radar Sonar Navig., 2002, 149(1):45-50.
[35] Lombardini F, Reigber A. Adaptive spectral estimation for multibaseline SAR tomography with airborne L-band data. International Geoscience and Remote Sensing Symposium (IGARSS), Toulouse, France, 2003: 2014-2016.
[36] Guillaso S, Reigber A. Scatterer characterisation using polarimetric SAR tomography. International Geoscience and Remote Sensing Symposium (IGARSS), Seoul, Korea, 2005:2685-2688.
[37] Nannini M, Scheiber R. A time domain beamforming algorithm for SAR tomography. 6th European Conference on Synthetic Aperture Radar (EUSAR), 2006: Paper No.107.
[38] Berardino P, Fornaro G, Lanari R, et al. Multi-pass synthetic aperture radar for 3-D focusing. International Geoscience and Remote Sensing Symposium (IGARSS), Toronto, Canada,2002:176--178.
[39] Fornaro G, Serafino F, Soldovieri F. Three dimensional focusing with multipass SAR data. IEEE Trans. Geosci. Remote Sens., 2003, 41(4): 507-517.
[40] Fornaro G, Serafino F. Spaceborne 3D SAR tomgography: experiments with ERS data. International Geoscience and Remote Sensing Symposium (IGARSS), Anchorage, Alaska, USA, 2004: 1240-1243.
[41] Fornaro G, Pauciullo A. LMMSE 3-D SAR focusing. IEEE Trans. Geosci. Remote Sens., 2009, 47(1):214-223.
[42] Reigber A, Neumann M, Guillaso S, et al. Evaluating PolInSAR parameter estimation using tomographic imaging results. European Radar Conference (EURAD), ESRIN, Frascati, Italy, 2005:189-192.
[43] Fornaro G, Lombardini F, Serafino F. Three-dimensional multipass SAR focusing: experiments with long-term spaceborne data. IEEE Trans. Geosci. Remote Sens., 2005, 43(4): 702-714.
[44] Lombardini F. Differential tomography: a new framework for SAR interferometry. International Geoscience and Remote Sensing Symposium (IGARSS), Toulouse, France, 2003:1206-1208.
[45] Lombardini F. Differential tomography: a new framework for SAR interferometry. IEEE Trans. Geosci. Remote Sens., 2005, 43(1):37-44.
[46] Serafino F, Soldovieri F, Lombardini F, et al. Singular value decomcoposition applied to 4D SAR imaging. International Geoscience and Remote Sensing Symposium (IGARSS), Seoul, Korea, 2005:2701-2704.
[47] Fornaro G, Serafino F. Imaging of signle and doule scatterers in urban areas via SAR tomography. IEEE Trans. Geosci. Remote Sens., 2006, 44(12):3497-3505.
[48] Fornaro G, Lombardini F, Pardini M, et al. Spaceborne multi-dimensional SAR imaging: current status and perspectives. International Geoscience and Remote Sensing Symposium (IGARSS), Barcelona, Spain, 2007: 5277-5280.
[49] Meglio F, Panariello G, Schirinzi G. Three dimensional SAR image focusing from non-uniform samples. International Geoscience and Remote Sensing Symposium (IGARSS), Barcelona, Spain, 2007: 528-531.
[50] Chen H, Davalan K. Super-resolution processing for polarimetric synthetic aperture radar tomography. IEEE National Radar Conference, Waltham, MA, USA, 2007: 618-623.
[51] Othmar F, Felix M, Erich M. Tomographic processing of multi-baseline P-band SAR data for imaging of a forested area. International Geoscience and Remote Sensing Symposium (IGARSS), Barcelona, Spain, 2007: 156-159.
[52] Scheiber R, Prats P, et al. Advances in airborne SAR interfereometry using the experimental SAR system of DLR. European Radar Conference, Munich, Germany, 2007: 91-94.
[53] Rolf S, Pau P, Matteo N, et al. Advances in airborne SAR interferometry using the Experimental SAR system of DLR. European Radar Conference (EURAD), Munich, Germany, 2007:91-94.
[54] Lombardini F, Pardini M, Gini F. Sector Interpolation for 3D SAR imaging with baseline diversity data. International Waveform Diversity and Design Conference (WDD), Pisa, Italy, 2007: 297-301.
[55] Lombardini F, Pardini M. 3-D SAR tomography: the multibaseline sector interpolation approach. IEEE Geosci. Remote Sens. Lett., 2008, 5(4): 630-634.
[56] Tebaldini S. Forest SAR tomography: a convariance matching approach. IEEE Radar Conference (RADAR), Roma, Italy, 2008, CDROM.
[57] Nannini M, Scheiber R, Moreira A. On the minimum number of tracks for SAR tomography. International Geoscience and Remote Sensing Symposium (IGARSS), Boston, USA, 2008:Ⅱ441-Ⅱ444.
[58] Tebaldini S, Rocca F, Guarnieri A. Model based SAR tomography of forested areas. International Geoscience and Remote Sensing Symposium(IGARSS), Boston, USA, 2008:Ⅱ593-Ⅱ596.
[59] Li L, Li F. Ionosphere tomography based on spaceborne SAR. Advances in Space Research, 2008, 42(7):1187-1193.
[60] Lombardini F, Fornaro G, Pardini M, et al. SAR tomography for scene elevation and deformation reconstruction: algorithms and potentialities. IEEE Radar Conference (RADAR), Roma, Italy, 2008, CDROM.
[61] Reale D, Pascazio V, Schirinzi G, et al. 3D imaging of ground based SAR data. International Geoscience and Remote Sensing Symposium(IGARSS), Boston, USA, 2008:Ⅳ1280-Ⅳ1283.
[62] Fornaro G, Reale D, Serafino F. 4D SAR focusing: a tool for improved imaging and monitoring of urban area. International Geoscience and Remote Sensing Symposium (IGARSS), Boston, USA, 2008:Ⅴ475-Ⅴ478.
[63] Chen H, Kasilinqam D. Performance analusis of multivariate super-resolution processing of polarimetric synthetic aperture radar tomography. International Geoscience and Remote Sensing Symposium(IGARSS), Boston, USA, 2008:Ⅳ169-Ⅳ172.
[64] Tebaldini S, Rocca F. On the impact of propagation disturbances on SAR tomography: Analysis and compensation. IEEE Radar Conference (RADAR), Pasadena, California, USA, 2009, CDROM.
[65] Fornaro G, Reale D, Serafino F. Four-dimensional SAR imaging for height estimation and monitoring of single and double scatterers. IEEE Trans. Geosci. Remote Sens., 2009, 47(1):224-237.
[66] Lombardini F, Cai F, Pardini M. Parametric differential SAR tomography of decorrelating volume scatterers. European Radar Conference (EuRAD), Rome, Italy, 2009: 270-273.
[67] Nannini M, Scheiber R, Moreira A. Estimation of the minimum number of tracks for SAR tomography. IEEE Trans. Geosci. Remote Sens., 2009, 47(2):531-543.
[68] Lombardini F, Pardini M, Fornaro G, et al. Linear and adaptive spaceborne three-dimensional SAR tomography: a comparison on real data. IET Radar Sonar Navig., 2009, 3(4): 424-436.
[69] Lombardini F, Pardini M. Detection of scatterer multiplicity in spaceborne SAR tomography with array errors. IEEE Radar Conference, Pasadena, California, USA, 2009, CDROM.
[70] Antonio D M, Fornaro G, Pauciullo A. Detection of single scatterers in multidimensional SAR imaging. IEEE Trans. Geosci. Remote Sens., 2009, 47(7): 2284-2297.
[71]任笑真,杨汝良.利用FB-MAPES算法估计Tomography SAR高度维信号源数.电子与信息学报, 2009, 31(7):1669-1673.
[72] Tan W, Wang Y, Hong W, et al. SAR three-dimensional imaging experiments with microwave anechoic chamber SAR data. Asia-Pacific Microwave Conference (APMC), Bangkok, Thailand, 2007: CDROM.
[73] Tan W, Hong W, Wang Y, et al. A novel imaging approach for multi-baseline SAR tomography. Asian and Pacific Conference on Synthetic Aperture Radar (APSAR), Huangshan, China, 2007: 423-426.
[74] Wang B, Wang Y, Hong W. Simulation research of parametric methods for multi-baseline SAR tomography. Asian and Pacific Conference on Synthetic Aperture Radar (APSAR), Huangshan, China, 2007: 203-206.
[75] Li J, Min R, Pi Y. SAR stereo imaging algorithm based on amplitude and phase estimation. Asian and Pacific Conference on Synthetic Aperture Radar (APSAR), Huangshan, China, 2007: 385-388.
[76] Herman G T. Fundamentals of computerized tomography: image reconstruction from projection. Springer, 2009:1-152.
[77] Brooks R A, Dichiro G. Principles of computer-assisted tomography in radiographic and radioisotopic imaging. Phys. Med. Biol., 1976, 21(5): 689-732.
[78] Colsher J G. Fully three-dimensional positron emission tomography. Phys. Med. Biol., 1980, 25(1): 103-115
[79] Mersereau R M, Oppenheim A V. Digital reconstruction of multidimensional signals from their projections. IEEE Proceedings, 1974, 62(10): 1319-1338.
[80] http://en.wikipedia.org/wiki/Computed_tomography.
[81]庄天戈. CT原理与算法.上海:上海交通大学出版社, 1992: 1-60.
[82] David C M, James D O, Jenkis W K. A tomographic formulation of spotlight-mode synthetic aperture radar. Proceedings of The IEEE, 1983, 71(8): 917-925.
[83] Abdelfattah R, Nicolas J M. InSAR image coregistration using the Fourier-Mellin transform. Int. J. Remote Sens., 2005, 26(13): 2865-2876.
[84] Scheiber R, Moreira A. Coregistration of interferometric SAR images using spectral diversity. IEEE Trans. Geosci. Remote Sens., 2000, 38(5): 2179-2191.
[85] Moreira A, Scheiber R. A new method for accurate co-registration of interferometric SAR images. International Geoscience and Remote Sensing Symposium (IGARSS), Piscataway, NJ, USA, 1998: 1091-1093.
[86] Scheiber R, Moreira A. Improving co-registration accuracy of interferometric SAR images using spectral diversity. International Geoscience and Remote Sensing Symposium (IGARSS), Piscataway, NJ, USA, 1999: 1709-1711.
[87] Marinkovic P, Hanssen R. Advanced InSAR coregistration using point clusters. International Geoscience and Remote Sensing Symposium (IGARSS), Anchorage, Alaska, USA, 2004: 489-492.
[88] Frandesco S. SAR Image Co-registration Based on Isolated Point Scatterers. IEEE Geosci Remote Sens. Lett., 2006, 3(3): 354-358.
[89] Bentoutou Y, Talen N, Bounoua A, et al. Feature based registration of satellite images. Int. Conf. Digit. Signal Process. (DSP), Wales, United Kingdom, 2007: 419-422.
[90] Kumar S, Richiwal V, Joglekar P N. Area based approach to registration of SAR images with sub-pixel accuracy. IEEE Reg 10 Annu Int Conf Proc TENCON, Hong Kong, China, 2006, CDROM.
[91] Anthony Y, Lilla Z, Tina K. A variational framework for joint segmentation and registration. Proc Workshop Math Methods Biomed Image Anal (MMBIA), Kauai, HI, USA, 2001: 44-51.
[92] Chen Y, Thiruvenkadam S, Huang F. Simultaneous segmentation and registration for functional MR images. Proc. Int. Conf. Pattern Recognit., 2002: 747-750.
[93] Marc D, Wolfgang R. A Mumford-Shah level-set approach for geometric image registration. SIAM Journal on Applied mathematics, 2006, 66(6): 2127-2148.
[94] Marc D, Martin R. Multiscale joint segmentation and registration of image morphology. IEEE Trans. Pattern Anal. Mach. Intell., 2007, 29(12):2181-2194.
[95] Sethian J A. Curvature and the evolution of fronrs. Communication of Mathematical Physics, 1985, 101(4): 487-502.
[96] Kass M, Witkin A, Terzopoulos D. Snakes: active contour models. Int. J. Comput. Vis., 1988, 1(4): 321-331.
[97] Osher S, Sethian J A. Fronts propagating with curvature dependent speed: algorithms based on the Hamilton-Jacobi formulation. J. comput. Phys., 1988, 79(1): 12- 49.
[98] Mumford D, Shah J. Optimal approximation by piecewise smooth functions and associated vatiational problems. Comm. Pure Appl. Math., 1989, 42(1):577-685.
[99] Tony F C, Luminita A V. Active contours without edges. IEEE Trans. Image Proc., 2001, 10(2): 266-277.
[100] Tsai R, Osher S. Level set methods and their applications in image science. Comm. Math. Science, 2003, 1(4): 623-656.
[101] James A C. Spectral estimation: an overdetermined rational model equation approach. Proc. IEEE, 1982, 70(9): 907-939.
[102] Georgios B G, Jerry M M. Cumulant-based order determination of non-gaussian ARMA models. IEEE Trans. Acoust Speech Signal Process, 1990, 38(8): 1411-1423.
[103] Hinich M H. Higher order cumulants and cumulant spectra. Circ. Syst. Signal Process., 1994, 13(4): 391-402.
[104] Petr T, Ananthram S. Statistical characterization of sample fourth-order cumulants of a noisy complex sinusoidal process. IEEE Trans. Signal Process., 1995, 43(7): 1620-1630.
[105]张贤达.现代信号处理.北京:清华大学出版社, 2002, 263-343.
[106] Huber P J. Projection pursuit. Ann. Stat., 1985, 13(2): 435-475.
[107] Mallat S G, Zhang Z. Matching pursuits with time-frequency dictionaries. IEEE Trans. Signal Process., 1993, 41(12): 3397-3415.
[108] Jiang C, Wan Q, Gui G, el al. Sparse multi-path channel estimation for OFDM systems. International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM), Dalian, China, 2008: 1-3.
[109] Yu X, Ling P, He Z, et al. Improving sequential Monte Carlo blind equalization in OFDM for sparse multipath channels. IEEE Worksshop Signal Process. Adv. Wireless Commun. (SPAWC), Helsinki, Finland, 2007: 1-5.
[110]李智杰,高新波,姬红兵.一种基于Curvelet变换的红外图像背景杂波抑制算法.红外技术, 2004, 26(5): 1-4.
[111]张跃飞,姜玉亭,王建英,等.基于稀疏分解的图像压缩.系统工程与电子技术, 2006, 28(4): 513-515.
[112]刘盛鹏,方勇.基于贝叶斯估计的Contourlet阀值图像降噪方法.计算机工程, 2007, 33(18): 31-33.
[113]王卫威,王正明,谢美华,等.基于稀疏约束与性能最优化的SAR图像增强方法.数据采集与处理, 2008, 23(3): 243-249.
[114] Potter L C, Schniter P, Ziniel J. Sparse reconstruction for RADAR. Proceedings of the SPIE, Orlando, FL, 2008: 3-15.
[115]杜小勇,胡卫东,郁文贤.高分辨雷达一维距离像稀疏表示技术.系统工程与电子技术, 2005, 27(6): 968-970.
[116]成萍,司锡才,姜义成,等.基于稀疏贝叶斯学习的稀疏信号表示ISAR成像方法.电子学报, 2008, 36(3): 547-550.
[117] Cetin M, Bossy E, Cleveland R, et al. Sparsity-driven sparse-aperture ultrasound imaging. IEEE International Conference on Acoustics Speech and Signal Processing, Toulouse, France, 2006:Ⅱ89-Ⅱ92.
[118] Cabrera S D, Parks T W. Extrapolation and spectral estimation with iterative weighted norm modification. IEEE Trans. Signal Processing, 1991, 39(4): 842-851.
[119] Wang J, Chen L, Yin Z. Array signal MP decomposition and its preliminary applications to DOA estimation. International Conference Intelligent Computing, Kunming, China, 2006: 775-778.
[120]邱天爽,毕晓晖.稀疏分量分析在欠定盲源分离问题中的研究进展及应用.信号处理, 2008, 24(6): 966-970
[121] Gorodnitsky I F, Rao B D. Sparse signal reconstruction from limited data using FOCUSS: a re-weighted minimum norm algorithm. IEEE Trans. Signal Process., 1997, 45(3): 600-616.
[122] Chen S S, Donoho D L, Saunders M A. Atomic decomposition by basis pursuit. SIAM J. Sci. Comput., 1999, 20(1): 33-61.
[123] Tropp J A. Greed is good: algorithmic results for sparse approximation. IEEE Tans. Inf. Theory, 2004, 50(10): 2231-2242.
[124] Donoho D L, Elad M, Temlyakov V N. Stable recovery of sparse overcomplete representations in the presence of noise. IEEE Trans. Inf. Theory, 2006, 52(1): 6-18.
[125] Wehner D R. High resolution radar. MA: Artech House, 1987: 1-47.
[126] Tipping M E. Sparse Bayesian learning and the relevance vector machine. J. Machine Learning Res., 2001, 1(3):211-244.
[127] Wipf D P, Palmer J A, Rao B D. Perspectives on sparse Bayesian learning. Neural Information Processing Systems (NIPS), Vanvouver and Whistler, British Columbia, Canada, 2003: 249-256.
[128] Wipf D P, Rao B D. Sparse Bayesian learning for basis selection. IEEE Trans. Signal Process., 2004, 52(8): 2153-2164.
[2] Curlander J C, McDonough R N. Synthetic aperture radar: systems and signal processing. New York: John Wiley & Sons, 1991, 1-225.
[3] Hein A. Processing of SAR data: fundamentals, signal processing, interferometry. Berlin: Springe, 2004, 1-280.
[4]皮亦鸣,杨建宇.合成孔径雷达成像原理.成都:电子科技大学出版社, 2007, 1-155.
[5]张直中.机载和星载合成孔径雷达导论.北京:电子工业出版社, 2004, 1-200.
[6] Cumming I G.合成孔径雷达成像—算法与实现(洪文,胡东辉,译).北京:电子工业出版社, 2007, 1-283.
[7]刘永坦.雷达成像技术.哈尔滨:哈尔滨工业大学出版社, 1999, 1-116.
[8]保铮,邢孟道,王彤.雷达成像技术.北京:电子工业出版社, 2005, 1-315.
[9]邢孟道,王彤.雷达信号处理基础.北京:电子工业出版社, 2008, 288-339.
[10] Maitre H.合成孔径雷达图像处理(孙洪译).北京:电子工业出版社, 2005, .1-103.
[11] Rogers A E, Ingalls R P. Venus: mapping the surface reflectivity by radar interferometry. Science, 1969, 165(3895): 797-799.
[12] Zebker H A, Goldstein R M. Topographic mapping from interferometric synthetic aperture radar observations. Journal of Geophysical Research, 91(B5): 4993-5000.
[13] Goldstein R M, Zebker H A, Werner C L. Satellite radar interferometry: two-dimensional phase unwrapping. Radio Science, 1988, 223(4): 713-720.
[14] Gabriel A K, Goldstein R M. Crossed orbit interferometry: theory and experiment results from SIRB. Int. J. Remote Sens., 1988, 9(5):857-872.
[15] Li F, Goldstein R M. Studies of multibaseline spaceborne interferometric synthetic aperture radars. IEEE Trans. Geosci. Remote Sens. 1990, 28(1): 88-97.
[16] Rodrigurez E, Martin J M. Theory and design of interferometric synthetic aperture radar. IEE Proc. Radar and Signal Processing, 1992, 139(2):147-159.
[17] Zebker H A, Cillasenor J. Decorrelation in interferometric radar echoes. IEEE Trans. Geosci. Remote Sens., 1992, 30(5): 950-959.
[18] Lin Q, Vesecky J, Zebker H. New approaches in interferometric SAR data processing. IEEE Trans. Geosci. Remote Sens., 1992, 30(3): 560-567.
[19] Bamler R, Hartl P. Synthetic aperture radar interferometry. Inverse Problems, 1998, 14(4): R1-R54.
[20] Rosen P A, Hensley S, Joughin I R, et al. Synthetic aperture radar interferometry. IEEE Proceedings, 2000, 88(3): 333-382.
[21]王超,张红,刘智.星载合成孔径雷达干涉测量.北京:科学出版社, 2002: 1-156.
[22] Knaell K. Three-dimensional SAR from practical apertures. Proceedings of SPIE, Sna Diego, CA, USA, 1995:31-41.
[23] Knaell K. Three-dimensional SAR from curvilinear apertures. IEEE Radar Conference (RADAR), Ann Arbor, Michigan, USA, 1996:220-225.
[24] Knaell K. Advances in three-dimensional SAR from curvilinear apertures. Proceedings of SPIE, San Diego, CA, USA, 1997: 178-184.
[25] Knaell K. Progress in three-dimensional SAR from curvilinear apertures. Proceedings of SPIE, San Diego, CA, USA, 1998: 110-121.
[26] Li J, Bi Z, Liu Z. Autofocus and feature extraction in curvilinear SAR via a relaxation-based algorithm. Proceedings of SPIE, Orlando, Fl, USA, 1998: 390-401.
[27] Su Z, Wu R, Liu J, et al. A robust sutofocus algorithm for the 3-D terget feature extraction in curvilinear SAR. CIE Int. Conf.Radar Proc., Beijing, China, 2001: 644-648.
[28] Su Z, Peng Y, Wang X. Evaluation of the aperture in the curvilinear SAR. CIE Int. Conf.Radar Proc., Shanghai, China, 2006, CDROM.
[29] Knaell K K, Cardillo G P. Radar tomography for the generation of three-dimensional images. IEE Eng. Radar Sonar Navig., 1995, 142(2): 54-60.
[30] Pasquali P, Prati C, Rocca F, et al. A 3D SAR experiment with EMSL data. International Geoscience and Remote Sensing Symposium (IGARSS), Firenze, Italy, 1995: 784–786.
[31] Reigber A, Moreira A, Papathanassiou K P. First demonstration of airborne SAR tomography using multibaseline L-band data. International Geoscience and Remote Sensing Symposium (IGARSS), Hamburg, Germany, 1999:44-46.
[32] Reigber A, Moreira A. First demonstration of airborne SAR tomography using multibaseline L-band data. IEEE Trans. Geosci. Remote Sens., 2000, 38(9): 2142-2152.
[33] She Z, Gray D A, Bogner R E, et al. Three-dimensional spaceborne synthetic aperture radar(SAR) imaging with multipass processing. Int. J. Remote Sens., 23(4):4357-4382.
[34] Homer J, Longstaff I D, She Z, et al. High resolution 3-D imaging via multi-pass SAR. IEE Proc. Radar Sonar Navig., 2002, 149(1):45-50.
[35] Lombardini F, Reigber A. Adaptive spectral estimation for multibaseline SAR tomography with airborne L-band data. International Geoscience and Remote Sensing Symposium (IGARSS), Toulouse, France, 2003: 2014-2016.
[36] Guillaso S, Reigber A. Scatterer characterisation using polarimetric SAR tomography. International Geoscience and Remote Sensing Symposium (IGARSS), Seoul, Korea, 2005:2685-2688.
[37] Nannini M, Scheiber R. A time domain beamforming algorithm for SAR tomography. 6th European Conference on Synthetic Aperture Radar (EUSAR), 2006: Paper No.107.
[38] Berardino P, Fornaro G, Lanari R, et al. Multi-pass synthetic aperture radar for 3-D focusing. International Geoscience and Remote Sensing Symposium (IGARSS), Toronto, Canada,2002:176--178.
[39] Fornaro G, Serafino F, Soldovieri F. Three dimensional focusing with multipass SAR data. IEEE Trans. Geosci. Remote Sens., 2003, 41(4): 507-517.
[40] Fornaro G, Serafino F. Spaceborne 3D SAR tomgography: experiments with ERS data. International Geoscience and Remote Sensing Symposium (IGARSS), Anchorage, Alaska, USA, 2004: 1240-1243.
[41] Fornaro G, Pauciullo A. LMMSE 3-D SAR focusing. IEEE Trans. Geosci. Remote Sens., 2009, 47(1):214-223.
[42] Reigber A, Neumann M, Guillaso S, et al. Evaluating PolInSAR parameter estimation using tomographic imaging results. European Radar Conference (EURAD), ESRIN, Frascati, Italy, 2005:189-192.
[43] Fornaro G, Lombardini F, Serafino F. Three-dimensional multipass SAR focusing: experiments with long-term spaceborne data. IEEE Trans. Geosci. Remote Sens., 2005, 43(4): 702-714.
[44] Lombardini F. Differential tomography: a new framework for SAR interferometry. International Geoscience and Remote Sensing Symposium (IGARSS), Toulouse, France, 2003:1206-1208.
[45] Lombardini F. Differential tomography: a new framework for SAR interferometry. IEEE Trans. Geosci. Remote Sens., 2005, 43(1):37-44.
[46] Serafino F, Soldovieri F, Lombardini F, et al. Singular value decomcoposition applied to 4D SAR imaging. International Geoscience and Remote Sensing Symposium (IGARSS), Seoul, Korea, 2005:2701-2704.
[47] Fornaro G, Serafino F. Imaging of signle and doule scatterers in urban areas via SAR tomography. IEEE Trans. Geosci. Remote Sens., 2006, 44(12):3497-3505.
[48] Fornaro G, Lombardini F, Pardini M, et al. Spaceborne multi-dimensional SAR imaging: current status and perspectives. International Geoscience and Remote Sensing Symposium (IGARSS), Barcelona, Spain, 2007: 5277-5280.
[49] Meglio F, Panariello G, Schirinzi G. Three dimensional SAR image focusing from non-uniform samples. International Geoscience and Remote Sensing Symposium (IGARSS), Barcelona, Spain, 2007: 528-531.
[50] Chen H, Davalan K. Super-resolution processing for polarimetric synthetic aperture radar tomography. IEEE National Radar Conference, Waltham, MA, USA, 2007: 618-623.
[51] Othmar F, Felix M, Erich M. Tomographic processing of multi-baseline P-band SAR data for imaging of a forested area. International Geoscience and Remote Sensing Symposium (IGARSS), Barcelona, Spain, 2007: 156-159.
[52] Scheiber R, Prats P, et al. Advances in airborne SAR interfereometry using the experimental SAR system of DLR. European Radar Conference, Munich, Germany, 2007: 91-94.
[53] Rolf S, Pau P, Matteo N, et al. Advances in airborne SAR interferometry using the Experimental SAR system of DLR. European Radar Conference (EURAD), Munich, Germany, 2007:91-94.
[54] Lombardini F, Pardini M, Gini F. Sector Interpolation for 3D SAR imaging with baseline diversity data. International Waveform Diversity and Design Conference (WDD), Pisa, Italy, 2007: 297-301.
[55] Lombardini F, Pardini M. 3-D SAR tomography: the multibaseline sector interpolation approach. IEEE Geosci. Remote Sens. Lett., 2008, 5(4): 630-634.
[56] Tebaldini S. Forest SAR tomography: a convariance matching approach. IEEE Radar Conference (RADAR), Roma, Italy, 2008, CDROM.
[57] Nannini M, Scheiber R, Moreira A. On the minimum number of tracks for SAR tomography. International Geoscience and Remote Sensing Symposium (IGARSS), Boston, USA, 2008:Ⅱ441-Ⅱ444.
[58] Tebaldini S, Rocca F, Guarnieri A. Model based SAR tomography of forested areas. International Geoscience and Remote Sensing Symposium(IGARSS), Boston, USA, 2008:Ⅱ593-Ⅱ596.
[59] Li L, Li F. Ionosphere tomography based on spaceborne SAR. Advances in Space Research, 2008, 42(7):1187-1193.
[60] Lombardini F, Fornaro G, Pardini M, et al. SAR tomography for scene elevation and deformation reconstruction: algorithms and potentialities. IEEE Radar Conference (RADAR), Roma, Italy, 2008, CDROM.
[61] Reale D, Pascazio V, Schirinzi G, et al. 3D imaging of ground based SAR data. International Geoscience and Remote Sensing Symposium(IGARSS), Boston, USA, 2008:Ⅳ1280-Ⅳ1283.
[62] Fornaro G, Reale D, Serafino F. 4D SAR focusing: a tool for improved imaging and monitoring of urban area. International Geoscience and Remote Sensing Symposium (IGARSS), Boston, USA, 2008:Ⅴ475-Ⅴ478.
[63] Chen H, Kasilinqam D. Performance analusis of multivariate super-resolution processing of polarimetric synthetic aperture radar tomography. International Geoscience and Remote Sensing Symposium(IGARSS), Boston, USA, 2008:Ⅳ169-Ⅳ172.
[64] Tebaldini S, Rocca F. On the impact of propagation disturbances on SAR tomography: Analysis and compensation. IEEE Radar Conference (RADAR), Pasadena, California, USA, 2009, CDROM.
[65] Fornaro G, Reale D, Serafino F. Four-dimensional SAR imaging for height estimation and monitoring of single and double scatterers. IEEE Trans. Geosci. Remote Sens., 2009, 47(1):224-237.
[66] Lombardini F, Cai F, Pardini M. Parametric differential SAR tomography of decorrelating volume scatterers. European Radar Conference (EuRAD), Rome, Italy, 2009: 270-273.
[67] Nannini M, Scheiber R, Moreira A. Estimation of the minimum number of tracks for SAR tomography. IEEE Trans. Geosci. Remote Sens., 2009, 47(2):531-543.
[68] Lombardini F, Pardini M, Fornaro G, et al. Linear and adaptive spaceborne three-dimensional SAR tomography: a comparison on real data. IET Radar Sonar Navig., 2009, 3(4): 424-436.
[69] Lombardini F, Pardini M. Detection of scatterer multiplicity in spaceborne SAR tomography with array errors. IEEE Radar Conference, Pasadena, California, USA, 2009, CDROM.
[70] Antonio D M, Fornaro G, Pauciullo A. Detection of single scatterers in multidimensional SAR imaging. IEEE Trans. Geosci. Remote Sens., 2009, 47(7): 2284-2297.
[71]任笑真,杨汝良.利用FB-MAPES算法估计Tomography SAR高度维信号源数.电子与信息学报, 2009, 31(7):1669-1673.
[72] Tan W, Wang Y, Hong W, et al. SAR three-dimensional imaging experiments with microwave anechoic chamber SAR data. Asia-Pacific Microwave Conference (APMC), Bangkok, Thailand, 2007: CDROM.
[73] Tan W, Hong W, Wang Y, et al. A novel imaging approach for multi-baseline SAR tomography. Asian and Pacific Conference on Synthetic Aperture Radar (APSAR), Huangshan, China, 2007: 423-426.
[74] Wang B, Wang Y, Hong W. Simulation research of parametric methods for multi-baseline SAR tomography. Asian and Pacific Conference on Synthetic Aperture Radar (APSAR), Huangshan, China, 2007: 203-206.
[75] Li J, Min R, Pi Y. SAR stereo imaging algorithm based on amplitude and phase estimation. Asian and Pacific Conference on Synthetic Aperture Radar (APSAR), Huangshan, China, 2007: 385-388.
[76] Herman G T. Fundamentals of computerized tomography: image reconstruction from projection. Springer, 2009:1-152.
[77] Brooks R A, Dichiro G. Principles of computer-assisted tomography in radiographic and radioisotopic imaging. Phys. Med. Biol., 1976, 21(5): 689-732.
[78] Colsher J G. Fully three-dimensional positron emission tomography. Phys. Med. Biol., 1980, 25(1): 103-115
[79] Mersereau R M, Oppenheim A V. Digital reconstruction of multidimensional signals from their projections. IEEE Proceedings, 1974, 62(10): 1319-1338.
[80] http://en.wikipedia.org/wiki/Computed_tomography.
[81]庄天戈. CT原理与算法.上海:上海交通大学出版社, 1992: 1-60.
[82] David C M, James D O, Jenkis W K. A tomographic formulation of spotlight-mode synthetic aperture radar. Proceedings of The IEEE, 1983, 71(8): 917-925.
[83] Abdelfattah R, Nicolas J M. InSAR image coregistration using the Fourier-Mellin transform. Int. J. Remote Sens., 2005, 26(13): 2865-2876.
[84] Scheiber R, Moreira A. Coregistration of interferometric SAR images using spectral diversity. IEEE Trans. Geosci. Remote Sens., 2000, 38(5): 2179-2191.
[85] Moreira A, Scheiber R. A new method for accurate co-registration of interferometric SAR images. International Geoscience and Remote Sensing Symposium (IGARSS), Piscataway, NJ, USA, 1998: 1091-1093.
[86] Scheiber R, Moreira A. Improving co-registration accuracy of interferometric SAR images using spectral diversity. International Geoscience and Remote Sensing Symposium (IGARSS), Piscataway, NJ, USA, 1999: 1709-1711.
[87] Marinkovic P, Hanssen R. Advanced InSAR coregistration using point clusters. International Geoscience and Remote Sensing Symposium (IGARSS), Anchorage, Alaska, USA, 2004: 489-492.
[88] Frandesco S. SAR Image Co-registration Based on Isolated Point Scatterers. IEEE Geosci Remote Sens. Lett., 2006, 3(3): 354-358.
[89] Bentoutou Y, Talen N, Bounoua A, et al. Feature based registration of satellite images. Int. Conf. Digit. Signal Process. (DSP), Wales, United Kingdom, 2007: 419-422.
[90] Kumar S, Richiwal V, Joglekar P N. Area based approach to registration of SAR images with sub-pixel accuracy. IEEE Reg 10 Annu Int Conf Proc TENCON, Hong Kong, China, 2006, CDROM.
[91] Anthony Y, Lilla Z, Tina K. A variational framework for joint segmentation and registration. Proc Workshop Math Methods Biomed Image Anal (MMBIA), Kauai, HI, USA, 2001: 44-51.
[92] Chen Y, Thiruvenkadam S, Huang F. Simultaneous segmentation and registration for functional MR images. Proc. Int. Conf. Pattern Recognit., 2002: 747-750.
[93] Marc D, Wolfgang R. A Mumford-Shah level-set approach for geometric image registration. SIAM Journal on Applied mathematics, 2006, 66(6): 2127-2148.
[94] Marc D, Martin R. Multiscale joint segmentation and registration of image morphology. IEEE Trans. Pattern Anal. Mach. Intell., 2007, 29(12):2181-2194.
[95] Sethian J A. Curvature and the evolution of fronrs. Communication of Mathematical Physics, 1985, 101(4): 487-502.
[96] Kass M, Witkin A, Terzopoulos D. Snakes: active contour models. Int. J. Comput. Vis., 1988, 1(4): 321-331.
[97] Osher S, Sethian J A. Fronts propagating with curvature dependent speed: algorithms based on the Hamilton-Jacobi formulation. J. comput. Phys., 1988, 79(1): 12- 49.
[98] Mumford D, Shah J. Optimal approximation by piecewise smooth functions and associated vatiational problems. Comm. Pure Appl. Math., 1989, 42(1):577-685.
[99] Tony F C, Luminita A V. Active contours without edges. IEEE Trans. Image Proc., 2001, 10(2): 266-277.
[100] Tsai R, Osher S. Level set methods and their applications in image science. Comm. Math. Science, 2003, 1(4): 623-656.
[101] James A C. Spectral estimation: an overdetermined rational model equation approach. Proc. IEEE, 1982, 70(9): 907-939.
[102] Georgios B G, Jerry M M. Cumulant-based order determination of non-gaussian ARMA models. IEEE Trans. Acoust Speech Signal Process, 1990, 38(8): 1411-1423.
[103] Hinich M H. Higher order cumulants and cumulant spectra. Circ. Syst. Signal Process., 1994, 13(4): 391-402.
[104] Petr T, Ananthram S. Statistical characterization of sample fourth-order cumulants of a noisy complex sinusoidal process. IEEE Trans. Signal Process., 1995, 43(7): 1620-1630.
[105]张贤达.现代信号处理.北京:清华大学出版社, 2002, 263-343.
[106] Huber P J. Projection pursuit. Ann. Stat., 1985, 13(2): 435-475.
[107] Mallat S G, Zhang Z. Matching pursuits with time-frequency dictionaries. IEEE Trans. Signal Process., 1993, 41(12): 3397-3415.
[108] Jiang C, Wan Q, Gui G, el al. Sparse multi-path channel estimation for OFDM systems. International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM), Dalian, China, 2008: 1-3.
[109] Yu X, Ling P, He Z, et al. Improving sequential Monte Carlo blind equalization in OFDM for sparse multipath channels. IEEE Worksshop Signal Process. Adv. Wireless Commun. (SPAWC), Helsinki, Finland, 2007: 1-5.
[110]李智杰,高新波,姬红兵.一种基于Curvelet变换的红外图像背景杂波抑制算法.红外技术, 2004, 26(5): 1-4.
[111]张跃飞,姜玉亭,王建英,等.基于稀疏分解的图像压缩.系统工程与电子技术, 2006, 28(4): 513-515.
[112]刘盛鹏,方勇.基于贝叶斯估计的Contourlet阀值图像降噪方法.计算机工程, 2007, 33(18): 31-33.
[113]王卫威,王正明,谢美华,等.基于稀疏约束与性能最优化的SAR图像增强方法.数据采集与处理, 2008, 23(3): 243-249.
[114] Potter L C, Schniter P, Ziniel J. Sparse reconstruction for RADAR. Proceedings of the SPIE, Orlando, FL, 2008: 3-15.
[115]杜小勇,胡卫东,郁文贤.高分辨雷达一维距离像稀疏表示技术.系统工程与电子技术, 2005, 27(6): 968-970.
[116]成萍,司锡才,姜义成,等.基于稀疏贝叶斯学习的稀疏信号表示ISAR成像方法.电子学报, 2008, 36(3): 547-550.
[117] Cetin M, Bossy E, Cleveland R, et al. Sparsity-driven sparse-aperture ultrasound imaging. IEEE International Conference on Acoustics Speech and Signal Processing, Toulouse, France, 2006:Ⅱ89-Ⅱ92.
[118] Cabrera S D, Parks T W. Extrapolation and spectral estimation with iterative weighted norm modification. IEEE Trans. Signal Processing, 1991, 39(4): 842-851.
[119] Wang J, Chen L, Yin Z. Array signal MP decomposition and its preliminary applications to DOA estimation. International Conference Intelligent Computing, Kunming, China, 2006: 775-778.
[120]邱天爽,毕晓晖.稀疏分量分析在欠定盲源分离问题中的研究进展及应用.信号处理, 2008, 24(6): 966-970
[121] Gorodnitsky I F, Rao B D. Sparse signal reconstruction from limited data using FOCUSS: a re-weighted minimum norm algorithm. IEEE Trans. Signal Process., 1997, 45(3): 600-616.
[122] Chen S S, Donoho D L, Saunders M A. Atomic decomposition by basis pursuit. SIAM J. Sci. Comput., 1999, 20(1): 33-61.
[123] Tropp J A. Greed is good: algorithmic results for sparse approximation. IEEE Tans. Inf. Theory, 2004, 50(10): 2231-2242.
[124] Donoho D L, Elad M, Temlyakov V N. Stable recovery of sparse overcomplete representations in the presence of noise. IEEE Trans. Inf. Theory, 2006, 52(1): 6-18.
[125] Wehner D R. High resolution radar. MA: Artech House, 1987: 1-47.
[126] Tipping M E. Sparse Bayesian learning and the relevance vector machine. J. Machine Learning Res., 2001, 1(3):211-244.
[127] Wipf D P, Palmer J A, Rao B D. Perspectives on sparse Bayesian learning. Neural Information Processing Systems (NIPS), Vanvouver and Whistler, British Columbia, Canada, 2003: 249-256.
[128] Wipf D P, Rao B D. Sparse Bayesian learning for basis selection. IEEE Trans. Signal Process., 2004, 52(8): 2153-2164.