超宽带SAR浅埋目标成像与检测的理论和技术研究
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摘要
机载或车载超宽带合成孔径雷达(Synthetic Aperture Radar,SAR)能够实现大区域浅埋目标的快速探测,具有安全和高效的优点。本文结合实际系统研究了超宽带SAR浅埋目标成像与检测中存在的若干理论和技术问题。
传统超宽带SAR信息处理将成像与检测割裂开来,限制了最终检测性能的提高。因此本文提出了“成像和检测一体化框架”的思想,包括面向检测的成像和基于成像的检测。在此基础上,提出了一种面向检测的时频表示成像算法(Time-Frequency Representation Image Formation,TFRIF),该方法也可有效解决基于成像的检测这一技术难题,具有实际应用价值。
针对传统成像算法会引起浅埋目标定位误差和图像散焦的问题,提出了修正波前重构(Modified Wavefront Reconstruction,MWR)和浅地表BP(SubsurfaceBack-Projection,SBP)两种浅埋目标成像算法,具有较高的聚焦和定位精度;针对实际工作条件下无法获取埋设深度等先验信息,提出了图像域折射和色散影响校正(Image Domain Refraction and Dispersion Correction,IDRDC)的浅埋目标聚焦和定位方法,该方法可有效解决无先验信息时的不同埋设深度和土壤环境中浅埋目标的聚焦和定位问题。
为了进一步提高图像质量,提出了基于二维频域支撑区特性的射频干扰(Radio Frequency Interference,RFI)抑制技术和适合前视系统的地距平面多视配准技术,分别可在降低系统复杂度的同时保证RFI抑制性能和有效提高前视系统多视处理相干斑噪声抑制中的配准效率。
目前制约超宽带SAR浅埋目标探测实用化的主要问题是虚警太多,因此提取有效特征和设计合适鉴别器是提高浅埋目标检测性能的关键。本文研究了金属地雷和未爆物特征提取技术,提出了基于图像域的金属地雷双峰特征增强算法,在此基础上,提出了基于空间-波数分布(Space-Wavenumber Distribution,SWD)的金属地雷四维散射函数估计及其特征选择方法,提取了包含双峰特性及方位不变性的特征向量;并利用SWD和Hu不变矩,提取了未爆物的多方位特征。
对于浅埋目标鉴别这种小样本学习和一类分类问题,考虑到目标和杂波误判风险不同以及埋设环境多样性等因素,提出了模糊超球面支持向量机(FuzzyHyoerSphere Support Vector Machine、FHS-SVM)浅埋目标鉴别算法,并将证据框架理论应用于高斯核FHS-SVM超参数优化,有效降低了检测结果的总体误判风险,提高了不同探测环境下金属地雷和未爆物的鉴别性能。在此基础上,提出用描述未爆物散射多方位特性的隐马尔可夫模型核替换高斯核函数,进一步改善了FHS-SVM对未爆物的鉴别性能。
Air- or vehicle-borne ultra-wideband Synthetic Aperture Radar (SAR) can perform quick detection of shallow buried targets over large areas, which has the advantages of safety and efficiency. In this thesis, some theoretical and technical problems in ultra-wideband SAR shallow buried targets imaging and detection are studied on real systems.
In the traditional UWB SAR information processing, imaging and detection are separated, which limits the improvement of final detection performance. Therefore, in this thesis, the theory of the imaging and detection integrated framework is proposed, which includes the detection oriented imaging and the imaging based detection. Based on this framework, a detection oriented Time-Frequency Representation Image Formation (TFRIF) is proposed, which can also solve the technical problem of imaging based detection and has applicable value in practice.
For the problem of locating error and imaging focusing of shallow buried target caused by traditional image formations, two shallow buried target image formations, the Modified Wavefront Reconstruction (MWR) and the Subsurface Back-Projection (SBP) algorithms, are proposed, which have higher focusing and locating precision. According to the problem of no priori information of buried depth etc. in practical operating conditions, an Image Domain Refraction and Dispersion Correction (IDRDC) method for shallow buried targets focusing and locating is proposed, which can solve the problem of focusing and locating multiple targets with different buried depths in various soil environments without priori information.
In order to improve the image quality further, a Radio Frequency Interference (RFI) suppression method on the region of support characteristic in the 2-dimensional frequency domain and a ground-plane multi-look registration technique for forward-looking systems are proposed, which can ensure RFI suppression performance while reduce system complex and improve the registration efficiency of the multi-look processing to suppress speckle noise in forward-looking systems, respectively.
Nowadays, the major challenge limiting the practical use of UWB SAR in shallow buried targets detection is the too many false alarms. Therefore, efficient feature extraction and suitable discriminator design are key points to improve the shallow buried targets detection performance. In this thesis, the feature extraction technique for metallic landmines and unexploded ordnances is studied. For a metallic landmine target, a double-peak feature enhancement algorithm in the image domain and a Space-Wavenumber Distribution (SWD) based 4-dimensional scattering function estimation and associated feature selection method are proposed, which can extract the feature vector with the double-peak and aspect-invariance characteristics. For an unexploded ordnance target, based on the SWD and the Hu moment invariants, the multi-aspect feature is extracted.
For the small sample learning and the one-class classification problems as shallow buried targets discrimination, considering the factors of the misclassification risk difference between targets and clutter and the buried environment diversity, the Fuzzy HyperSphere Support Vector Machine (FHS-SVM) shallow buried target discrimination algorithm is proposed. Furthermore, the evidence framework is applied to the problem of the Gaussian kernel FHS-SVM hyperparameters optimization, which can reduce the total misclassification risk of detection result and improve the discrimination performance for both metallic landmines and unexploded ordnances in varying detection environments. Based on the FHS-SVM, the hidden Markov model (HMM) kernel, which describes the multi-aspect characteristic of unexploded ordnance, is used to replace the Gaussian kernel to improve the FHS-SVM discrimination performance for unexploded ordnances further.
传统超宽带SAR信息处理将成像与检测割裂开来,限制了最终检测性能的提高。因此本文提出了“成像和检测一体化框架”的思想,包括面向检测的成像和基于成像的检测。在此基础上,提出了一种面向检测的时频表示成像算法(Time-Frequency Representation Image Formation,TFRIF),该方法也可有效解决基于成像的检测这一技术难题,具有实际应用价值。
针对传统成像算法会引起浅埋目标定位误差和图像散焦的问题,提出了修正波前重构(Modified Wavefront Reconstruction,MWR)和浅地表BP(SubsurfaceBack-Projection,SBP)两种浅埋目标成像算法,具有较高的聚焦和定位精度;针对实际工作条件下无法获取埋设深度等先验信息,提出了图像域折射和色散影响校正(Image Domain Refraction and Dispersion Correction,IDRDC)的浅埋目标聚焦和定位方法,该方法可有效解决无先验信息时的不同埋设深度和土壤环境中浅埋目标的聚焦和定位问题。
为了进一步提高图像质量,提出了基于二维频域支撑区特性的射频干扰(Radio Frequency Interference,RFI)抑制技术和适合前视系统的地距平面多视配准技术,分别可在降低系统复杂度的同时保证RFI抑制性能和有效提高前视系统多视处理相干斑噪声抑制中的配准效率。
目前制约超宽带SAR浅埋目标探测实用化的主要问题是虚警太多,因此提取有效特征和设计合适鉴别器是提高浅埋目标检测性能的关键。本文研究了金属地雷和未爆物特征提取技术,提出了基于图像域的金属地雷双峰特征增强算法,在此基础上,提出了基于空间-波数分布(Space-Wavenumber Distribution,SWD)的金属地雷四维散射函数估计及其特征选择方法,提取了包含双峰特性及方位不变性的特征向量;并利用SWD和Hu不变矩,提取了未爆物的多方位特征。
对于浅埋目标鉴别这种小样本学习和一类分类问题,考虑到目标和杂波误判风险不同以及埋设环境多样性等因素,提出了模糊超球面支持向量机(FuzzyHyoerSphere Support Vector Machine、FHS-SVM)浅埋目标鉴别算法,并将证据框架理论应用于高斯核FHS-SVM超参数优化,有效降低了检测结果的总体误判风险,提高了不同探测环境下金属地雷和未爆物的鉴别性能。在此基础上,提出用描述未爆物散射多方位特性的隐马尔可夫模型核替换高斯核函数,进一步改善了FHS-SVM对未爆物的鉴别性能。
Air- or vehicle-borne ultra-wideband Synthetic Aperture Radar (SAR) can perform quick detection of shallow buried targets over large areas, which has the advantages of safety and efficiency. In this thesis, some theoretical and technical problems in ultra-wideband SAR shallow buried targets imaging and detection are studied on real systems.
In the traditional UWB SAR information processing, imaging and detection are separated, which limits the improvement of final detection performance. Therefore, in this thesis, the theory of the imaging and detection integrated framework is proposed, which includes the detection oriented imaging and the imaging based detection. Based on this framework, a detection oriented Time-Frequency Representation Image Formation (TFRIF) is proposed, which can also solve the technical problem of imaging based detection and has applicable value in practice.
For the problem of locating error and imaging focusing of shallow buried target caused by traditional image formations, two shallow buried target image formations, the Modified Wavefront Reconstruction (MWR) and the Subsurface Back-Projection (SBP) algorithms, are proposed, which have higher focusing and locating precision. According to the problem of no priori information of buried depth etc. in practical operating conditions, an Image Domain Refraction and Dispersion Correction (IDRDC) method for shallow buried targets focusing and locating is proposed, which can solve the problem of focusing and locating multiple targets with different buried depths in various soil environments without priori information.
In order to improve the image quality further, a Radio Frequency Interference (RFI) suppression method on the region of support characteristic in the 2-dimensional frequency domain and a ground-plane multi-look registration technique for forward-looking systems are proposed, which can ensure RFI suppression performance while reduce system complex and improve the registration efficiency of the multi-look processing to suppress speckle noise in forward-looking systems, respectively.
Nowadays, the major challenge limiting the practical use of UWB SAR in shallow buried targets detection is the too many false alarms. Therefore, efficient feature extraction and suitable discriminator design are key points to improve the shallow buried targets detection performance. In this thesis, the feature extraction technique for metallic landmines and unexploded ordnances is studied. For a metallic landmine target, a double-peak feature enhancement algorithm in the image domain and a Space-Wavenumber Distribution (SWD) based 4-dimensional scattering function estimation and associated feature selection method are proposed, which can extract the feature vector with the double-peak and aspect-invariance characteristics. For an unexploded ordnance target, based on the SWD and the Hu moment invariants, the multi-aspect feature is extracted.
For the small sample learning and the one-class classification problems as shallow buried targets discrimination, considering the factors of the misclassification risk difference between targets and clutter and the buried environment diversity, the Fuzzy HyperSphere Support Vector Machine (FHS-SVM) shallow buried target discrimination algorithm is proposed. Furthermore, the evidence framework is applied to the problem of the Gaussian kernel FHS-SVM hyperparameters optimization, which can reduce the total misclassification risk of detection result and improve the discrimination performance for both metallic landmines and unexploded ordnances in varying detection environments. Based on the FHS-SVM, the hidden Markov model (HMM) kernel, which describes the multi-aspect characteristic of unexploded ordnance, is used to replace the Gaussian kernel to improve the FHS-SVM discrimination performance for unexploded ordnances further.
引文
[1]Curlander,J.C.and McDonough,R.N.,Synthetic Aperture Radar:Systems and Signal Processing,New York:Wiley,1991
[2]张直中,微波成像术,北京:科学出版社,1990
[3]张澄波,综合孔径雷达原理、系统分析与应用,北京:科学出版社,1989
[4]Farina,A.and Studer,F.A.,Detection with High Resolution Radar:Advanced Topics & potential Applications,Chinese Journal of Systems Engineering and Electronics,1992,3(1):21-34
[5]Fowler,C.,Entzminger,J.and Corum,J.,Assessment of ultra-wideband(UWB)technology,IEEE Aerospace and Electronic Systems Magazine,1990,5(11):45-49
[6]Taylor,J.D.,Ultra-wideband radar technology,New York:CRC press,2001
[7]Binder,B.T.,Toups,M.F.,Ayasli,S.and Adams,E.M.,SAR foliage penetration phenomenology of tropical rain forest and northern U.S.forest,Alexandria,VA,USA,1995:158-163
[8]Soumekh,M.,Nobles,D.A.,Wicks,M.C.and Genello,G.J.,Signal processing of wide bandwidth and wide beamwidth P-3 SAR data,IEEE Transactions on Aerospace and Electronic Systems,2001,37(4):1122-1141
[9]Toups,M.F.,Bessette,L.A.and Binder,B.T.,Foliage penetration data collections and investigations utilizing the P-3 UWB SAR,Proceedings of SPIE,1996,2757:136-144
[10]MacDonald,D.,Chang,C.-F.,Roman,J.and Koesel,R.,Automatic detection and cueing for foliage-concealed targets,Proceedings of SPIE,1996,2757:152-162
[11]Davis,M.E.,Tomlinson,P.G.and Maloney,R.P.,Technical challenges in ultra-wideband radar development for target detection and terrain mapping,IEEE National Radar Conference-Proceedings,1999:1-6
[12]http://www.landmine.org
[13]Gao,P.and Collins,L.M.,A two-dimensional generalized likelihood ratio test for land mine and small unexploded ordnance detection,Signal Processing,2000,80(8):1669-1686
[14]常文革,UWB SAR系统设计与实现,长沙:国防科学技术大学博士学位论文,2001,10
[15]祝明波,UWB-SAR信号设计与产生技术研究,长沙:国防科学技术大学博士学位论文,1999,10
[16]黎向阳,UWB-SAR接收技术研究,长沙:国防科学技术大学博士学位论文,2000,3
[17]王顺华,机载大处理角UWB-SAR成像理论及算法研究,长沙:国防科学技术大学博士学位论文,1998,10
[18]李肖东,基于相参子孔径机载SAR高分辨成像技术研究,长沙:国防科学技术大学博士学位论文,2003,10
[19]董臻,UWB-SAR信息处理中的若干问题研究,长沙:国防科学技术大学博士学位论文,2001,10
[20]刘光平,超宽带合成孔径雷达高效成像算法,长沙:国防科学技术大学博士学 位论文,2003,3
[21]方学立,UWB SAR图像中的目标检测与鉴别,长沙:国防科学技术大学博士学位论文,2005,10
[22]蒋咏梅,叶簇穿透超宽带SAR目标检测方法研究,长沙:国防科学技术大学博士学位论文,1998,10
[23]王岩,机载SAR目标特征提取与识别方法研究,长沙:国防科学技术大学博士学位论文,2003,10
[24]黄晓涛,UWB-SAR抑制RFI方法研究,长沙:国防科学技术大学博士学位论文,1999,11
[25]郭微光,机载超宽带合成孔径雷达运动补偿技术研究,长沙:国防科学技术大学博士学位论文,2003,4
[26]Carin,L.,Geng,N.,McClure,M.,Sichina,J.and Nguyen,L.,Ultra-wide-band synthetic-aperture radar for mine-field detection,IEEE Antennas and Propagation Magazine,1999,41(1):18-33
[27]DeLuca,C.,Marinelli,V.,Ressler,M.and Ton,T.,Unexploded ordnance detection experiments at extensive,fully ground-truthed test sites at Yuma Proving Ground and Eglin AFB,Proceedings of SPIE,1999,3710(Ⅱ):1025-1034
[28]Geng,N.,Carin,L.,Ressler,M.and Merchant,B.,Ultrawideband SAR for detection of subsurface unexploded ordnance(UXO):measurement,modeling and signal processing,Proceedings of SPIE,1999,3704:75-83
[29]Happ,L.,Kappra,K.A.,Ressler,M.A.,Sichina,J.P.,Sturgess,K.and Le,F.,Low-frequency ultra-wideband synthetic aperture radar 1995 BoomSAR tests,IEEE National Radar Conference,Ann Arbor,MI,USA,1996:54-59
[30]Happ,L.,Le,F.,Ressler,M.and Kappra,K.,Low-frequency ultrawideband synthetic aperture radar:frequency subbanding for targets obscured by the ground,Proceedings of SPIE,1996,2747:194-201
[31]Kaplan,L.M.,Oh,S.-M.and McClellan,J.H.,Detection of broadside targets during image formation using a quadtree approach,IEEE National Radar Conference,Alexandria,VA,USA,2000:104-109
[32]Kaplan,L.M.,Oh,S.-M.and McClellan,J.H.,Multiresolution target discrimination during image formation,Proceedings of SPIE,2000,4053:239-250
[33]Nguyen,L.,Kappra,K.,Wong,D.,Ressler,M.and Sichina,J.,Mine detection performance in different soil conditions using data from an ultra-wideband wide-area surveillance radar,Proceedings of SPIE,1999,3710(Ⅱ):930-941
[34]Ressler,M.,Merchant,B.,Wong,D.,Nguyen,L.,Geng,N.and Carin,L.,Calibration of the ARL BoomSAR using rigorous scattering models for fiducial targets over ground,Proceedings of SPIE,1999,3704:50-56
[35]Ressler,M.A.,Army research laboratory ultra wideband boomSAR,International Geoscience and Remote Sensing Symposium(IGARSS),Lincoln,NE,USA,1996:1886-1888
[36]Showman,G.A.and McClellan,J.H.,Blind polarimetric calibration of ultra-wideband SAR imagery,Proceedings of SPIE,2000,4033:102-113
[37]Sichina,J.,Nguyen,L.and Sullivan,A.,Using models and measurements to describe ultra wideband radar-scattering phenomena,Proceedings of SPIE,2000,4053:220-228
[38]Sullivan,A.,Damarla,T.R.,Geng,N.,Dong,Y.and Carin,L.,Detection of above ground and subsurface unexploded ordnance using ultra-wideband (UWB) synthetic aperture radar (SAR) and electromagnetic modeling tools, Proceedings of SPIE, 2000,4038 (II):983-992
[39] Sullivan, A., Geng, N., Carin, L., Nguyen, L. and Sichina, J., Performance analysis for radar detection of buried anti-tank and anti-personnel land mines, Proceedings of SPIE, 1999,3710(II): 1043-1050
[40] Ton, T. and Nguyen, L., Evaluation of the quadtree SAR image formation algorithm, Proceedings of SPIE, 2000,4041:78-85
[41] Wong, D., Ressler, M., Ton, T. and Merchant, B., UWB SAR calibration of mine imagery, Proceedings of SPIE, 1999,3704:57-65
[42] Vickers, R. S., Design and applications of airborne radars in the VHF/UHF band, IEEE Aerospace and Electronic Systems Magazine, 2002,17(6):26-29
[43] Kositsky, J., Results from a forward-looking GPR mine detection system, Proceedings of SPIE, 2000,4038 (II): 1077-1087
[44] Kositsky, J. and Amazeen, C, Results from a forward-looking GPR mine detection system, Proceedings of SPIE, 2001,4394:700-711
[45] Kositsky, J., Cosgrove, R., Amazeen, C. and Milanfar, P., Results from a forward-looking GPR mine detection system, Proceedings of SPIE, 2002, 4742:206-217
[46] Bradley, M., Witten, T., Duncan, M. and McCummins, B., Mine detection with a forward-looking ground-penetrating synthetic aperture radar, Proceedings of SPIE, 2003, 5089:334-347
[47] Bradley, M., Witten, T, Duncan, M. and McCummins, B., Anti-tank and side-attack mine detection with a forward-looking GPR, Proceedings of SPIE, 2004,5415:421-432
[48] Liu, G, Wang, Y, Li, J. and Bradley, M., SAR imaging for a forward-looking GPR system, Proceedings of SPIE, 2003, 5089:322-333
[49] http://www.miragegpr.com
[50] http://www.mineseeker.org
[51] Andrieu, J., Gallais, F., Mallepeyre, V., Bertrand, V., Beillard, B., Jecko, B., Guillerey, R. and Le Goff, M., Land mine detection with an ultra-wideband SAR system, Proceedings of SPIE, 2002, 4742:237-247
[52] Delmote, P., Dubois, C, Andrieu, J., Beillard, B., Lalande, M., Bertrand, V., Jecko, B., Pecastaing, L., Gibert, A., Paillol, J., Domens, P., Guillerey, R., Monnier, F. and Legoff, M., The UWB SAR system Pulsar: New generator and antenna developments, Proceedings of SPIE, 2003, 5077:223-234
[53] Homer, J., Tang, H. T. and Longstaff, I. D., Radar imaging of shallow buried objects, International Geoscience and Remote Sensing Symposium (IGARSS), Hamburg, Ger, 1999:2477-2479
[54] Lloyd, D. and Longstaff, I. D., Ultra-wideband multistatic SAR for the detection and location of landmines, IEE Proceedings: Radar, Sonar and Navigation, 2003, 150(3):158-164
[55] Stickley, G. F., Noon, D. A., Cherniakov, M. and Longstaff, I. D., Preliminary field results of an ultra-wideband (10-620 MHz) stepped-frequency ground penetrating radar, International Geoscience and Remote Sensing Symposium (IGARSS), Singapore, 1997:1282-1284
[56] Wang, Y, Longstaff, I. D. and Leat, C. J., SAR. imaging of buried objects from MoM modelled scattered field, IEE Proceedings: Radar, Sonar and Navigation, 2001,148(3):167-172
[57]Bamler,R.,A comparison of range-Doppler and wavenumber domain SAR focusing algorithms,IEEE Transactions on Geoscience and Remote Sensing,1992,30(4):706-713
[58]Cafforio,C.,Prati,C.and Rocca,F.,SAR data focusing using seismic migration techniques,IEEE Transactions on Aerospace and Electronic Systems,1991,27(2):194-207
[59]Prati,C.,Rocca,F.,Guarnieri,A.M.and Damonti,E.,Seismic migration for SAR focusing:Interferometrical applications,IEEE Transactions on Geoscience and Remote Sensing,1990,28(4):627-640
[60]Soumekh,M.,Synthetic aperture radar signal processing with MATLAB algorithm,New York:John Wiley & Sons,INC,1999
[61]Barber,B.C.,Theory of digital imaging from orbital synthetic-aperture radar,International Journal of Remote Sensing,1985,6(7):1009-1057
[62]Hellsten,H.,Ulander,L.M.,Gustavsson,A.and Larsson,B.,Development of VHF CARABAS Ⅱ SAR,Proceedings of SPIE,1996,2747:48-60
[63]McCorkle,J.W.and Rofheart,M.,An order N21og(N) backprojector algorithm for focusing wide-angle wide-bandwidth arbitrary-motion synthetic aperture radar,Proceedings of SPIE,1996,2747:25-36
[64]Ulander,L.M.H.,Hellsten,H.and Stenstrom,G.,Synthetic-aperture radar processing using fast factorized back-projection,IEEE Transactions on Aerospace and Electronic Systems,2003,39(3):760-776
[65]DiPietro,R.C.,Fante,R.L.,Perry,R.P.,Soumekh,M.and Tromp,L.D.,Multi-resolution FOPEN SAR image formation,Proceedings of SPIE,1999,3721:170-188
[66]Wang,Y.,Li,X.,Sun,Y.,Li,J.and Stoica,P.,Adaptive imaging for forward-looking ground penetrating radar,IEEE Transactions on Aerospace and Electronic Systems,2005,41(3):922-936
[67]Cai,L.and Walton,E.,SAR imaging through complex media,21 st annual meeting and symposium of Antenna Measurement Techniques Association,Monterey Bay,California,1999:131-134
[68]Fortuny-Guasch,J.,A novel 3-D subsurface radar imaging technique,IEEE Transactions on Geoscience and Remote Sensing,2002,40(2):443-452
[69]Groenenboom,J.and Yarovoy,A.G,Data processing for landmine detection dedicated GPR,Proceedings of SPIE,2000,4084:867-871
[70]Milisavljevic,N.and Yarovoy,A.G.,An effective algorithm for subsurface SAR imaging,AP-S International Symposium,San Antonio,TX,2002:314-317
[71]Gu,K.,Wang,G.and Li,J.,Migration based SAR imaging for ground penetrating radar systems,lEE Proceedings:Radar,Sonar and Navigation,2004,151(5):317-325
[72]Gough,P.T.and Hunt,B.R.,Synthetic aperture radar image reconstruction algorithms designed for subsurface imaging,International Geoscience and Remote Sensing Symposium(IGARSS),Singapore,1997:1588-1590
[73]Novak,L.M.,Halversen,S.D.,Owirka,G.J.and Hiett,M.,Effects of polarization and resolution on SAR ATR,IEEE Transactions on Aerospace and Electronic Systems,1997,33(1):102-116
[74]Geng,N.and Carin,L.,Resonances of a dielectric BOR buried in a lossy,dispersive layered medium,International Geoscience and Remote Sensing Symposium (IGARSS), Seattle, WA, USA, 1998:222-224
[75] Geng, N., Jackson, D. R. and Carin, L., On the resonances of a dielectric BOR buried in a dispersive layered medium, IEEE Transactions on Antennas and Propagation, 1999,47(8):1305-1313
[76] He, J., Yu, T., Geng, N. and Carin, L., Method of moments analysis of electromagnetic scattering from a general three-dimensional dielectric target embedded in a multilayered medium, Radio Science, 2000, 35(2):305-313
[77] Nguyen, L., Kapoor, R., Wong, D. and Sichina, J., Ultra-wideband radar target discrimination utilizing an advanced feature set, Proceedings of SPIE, 1998, 3370:289-306
[78] Nguyen, L., Kappra, K., Wong, D., Kapoor, R. and Sichina, J., A mine field detection algorithm utilizing data from an ultra-wideband wide-area surveillance radar, Proceedings of SPIE, 1998,3392:627-643
[79] Vitebskiy, S., Carin, L., Ressler, M. A. and Le, F. H., Ultra-wideband, short-pulse ground-penetrating radar: Simulation and measurement, IEEE Transactions on Geoscience and Remote Sensing, 1997, 35(3):762-772
[80] Casey, K. F. and Oetzel, G. N., Physical-optics models for scatterng from bured mines, Second Australian-American Joint Conference on the Technology of Mine Countermeasures, Sydney, Australia, 2001:1 -15
[81] Cosgrove, R. B., Milanfar, P. and Kositsky, J., Trained detection of buried mines in SAR images via the deflection-optimal criterion, IEEE Transactions on Geoscience and Remote Sensing, 2004,42(11):2569-2575
[82] Allen, M. R., Efficient approach to physics-based FOPEN-SAR ATD/R, Proceedings of SPIE, 1996,2757:163-172
[83] Allen, M. R., Jauregui, J. M. and Hoff, L. E., FOPEN-SAR detection by direct use of simple scattering physics, IEEE National Radar Conference, Alexandria, VA, USA, 1995:152-157
[84] Allen, M. R., Phillips, S. A. and Sofianos, D. J., Wide-angle SAR-matched filter image formation for enhanced detection performance, Proceedings of SPIE, 1994, 2093:381-387
[85] Chaney, R. D., Willsky, A. S. and Novak, L. M., Coherent aspect-dependent SAR image formation, Proceedings of SPIE, 1994, 2230:256-274
[86] Dong, Y, Runkle, P. R., Carin, L., Damarla, R., Sullivan, A., Ressler, M. A. and Sichina, J., Multi-aspect detection of surface and shallow-buried unexploded ordnance via ultra-wideband synthetic aperture radar, IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(6):1259-1270
[87] Carin, L., Geng, N., McClure, M., Dong, Y, Liu, Z., He, J., Sichina, J., Ressler, M., Nguyen, L. and Sullivan, A., Wide-area detection of land mines and unexploded ordnance, Inverse Problems, 2002,18(3):575-609
[88] Damarla, T. R., A Fast Algorithm for Computing Scattered Fields Using Physical Optics Equivalent Approximation in Half-Space, Army Research Laboratory Report, July 2000
[89] Tax, D., One-class classification: concept-learning in the absence of counter-examples, Deflt University of Technology, PhD thesis, 2001, June
[90] Soumekh, M., Reconnaissance with ultra wideband UHF synthetic aperture radar, IEEE Signal Processing Magazine, 1995,12(4):21-40
[91] Eaves, J. L. and Reedy, E. K., Principles of Moden Radar, New York:Van Nostrand Reinhold, 1987
[92] Kaplan, L. M, McClellan, J. H. and Oh, S.-M., Prescreening during image formation for ultrawideband radar, IEEE Transactions on Aerospace and Electronic Systems, 2002, 38(1):74-88
[93] Carin, L., Ybarra, G., Bharadwaj, P. and Runkle, P., Physics-based classification of targets in SAR imagery using subaperture sequences, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Phoenix, AZ, USA, 1999:3341-3344
[94] Runkle, P., Carin, L. and Nguyen, L., Multi-aspect target classification using hidden Markov models for data fusion, International Geoscience and Remote Sensing Symposium (IGARSS), Honolulu, HI, USA, 2000:2123-2125
[95] Runkle, P., Nguyen, L. and Carin, L., Multi-aspect target detection for SAR imagery using hidden Markov models and two-dimensional matching pursuits, Proceedings of SPIE, 2000,4053:251-259
[96] Runkle, P., Nguyen, L., Ybarra, G. and Carin, L., Multi-aspect target detection in SAR imagery using hidden Markov models, Proceedings of SPIE, 1999, 3721:214-223
[97] Runkle, P., Nguyen, L. H., McClellan, J. H. and Carin, L., Multi-aspect target detection for SAR imagery using hidden Markov models, IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(1):46-55
[98] Soumekh, M., Gunther, G., Linderman, M. and Kohler, R., Digitally-spotlighted subaperture SAR image formation using high performance computing, Proceedings of SPIE, 2000,4053:260-271
[99] Wong, D. and Carin, L., Analysis and processing of ultra wide-band SAR imagery for buried landmine detection, IEEE Transactions on Antennas and Propagation, 1998, 46(11):1747-1748
[100]Astanin, L. Y. and Kostylev, A. A., Utrawideband Radar Measurements Analysis and Processing, London, United Kingdom:The institution of Electrival Engineers, 1997
[101] 保铮等,雷达成像技术, 北京:电子工业出版社,2005
[102]Jakowatz, C. V. J., Wahl, D. E., Eichel, P. H., Ghiglia, D. C. and Thompson, P. A., Spotlight-Mode Synthetic Aperture Radar: A signal Processing Approach, Spinger, 1996
[103]Goodman, R., Tummala, S. and Carrara, W., Issues in ultra-wideband, widebeam SAR image formation, IEEE National Radar Conference, Alexandria, VA, USA, 1995:479-485
[104]王亮等, UWB SAR 成像中非正交旁瓣的抑制, 信号处理, 2006, 22(1):28-31
[105]McCorkle, J. W., Focusing of synthetic aperture ultra wideband data, International Geoscience and Remote Sensing Symposium (IGARSS), Fairborn, OH, USA, 1991:1-5
[106]Rau, R. and McClellan, J. H., Analytic models and postprocessing techniques for UWB SAR, IEEE Transactions on Aerospace and Electronic Systems, 2000, 36(4): 1058-1074
[107]Carrara, W. G, Goodman, R. S. and Majewski, R. M., Spotlight synthetic aperture radar: signal processing algorithms, Boston:Artech House, 1995
[108]Moreira, J. R., A new method of aircraft motion error extraction from radar raw data for real-time motion compensation, IEEE Transactions on Geoscience and Remote Sensing, 1990, 28(4):620-626
[109]Wahl, D. E., Eichel, P. H., Ghiglia, D. C. and Jakowatz, C. V., Jr., Phase gradient autofocus - a robust tool for high resolution SAR phase correction, IEEE Transactions on Aerospace and Electronic Systems, 1994, 30(3):827-835
[110]Thompson, D. G, Bates, J. S., Arnold, D. V., Long, D. G and Robertson, A., Range dependent phase gradient autofocus, International Geoscience and Remote Sensing Symposium (IGARSS), Seattle, WA, USA, 1998:2634-2636
[111]Potsis, A., Reigber, A., Mittermayer, J., Moreira, A. and Uzunoglou, N., Sub-aperture algorithm for motion compensation improvement in wide-beam SAR data processing, Electronics Letters, 2001, 37(23): 1405-1407
[112] Reigber, A., Potsis, A., Alivizatos, E., Uzunoglu, N. and Moreira, A., Wavenumber Domain SAR Focusing with Integrated Motion Compensation, International Geoscience and Remote Sensing Symposium (IGARSS), Toulouse, France, 2003:1465-1467
[113]Thayaparan, T., Lampropoulos, G., Wong, S. K. and Riseborough, E., Application of adaptive joint time-frequency algorithm for focusing distorted ISAR images from simulated and measured radar data, IEE Proceedings: Radar, Sonar and Navigation, 2003,150(4):213-220
[114]Wong, S. K., Duff, G. and Riseborough, E., Distortion in the inverse synthetic aperture radar (ISAR) images of a target with time-varying perturbed motion, IEE Proceedings: Radar, Sonar and Navigation, 2003,150(4):221-227
[115]Li, J. and Ling, H., Application of adaptive chirplet representation for ISAR feature extraction from targets with rotating parts, IEE Proceedings: Radar, Sonar and Navigation, 2003,150(4):284-291
[116]Choi, I. S., Cho, B. L. and Kim, H. T., ISAR motion compensation using evolutionary adaptive wavelet transform, IEE Proceedings: Radar, Sonar and Navigation, 2003,150(4):229-233
[117]Ovarlez, J. P., Vignaud, L., Castelli, J. C., Tria, M. and Benidir, M., Analysis of SAR images by multidimensional wavelet transform, IEE Proceedings: Radar, Sonar and Navigation, 2003,150(4):234-241
[118]Vignaud, L., Wavelet-RELAX feature extraction in radar images, IEE Proceedings: Radar, Sonar and Navigation, 2003,150(4):242-246
[119]Flandrin, P. and Martin, W., A general class of estimators for the Wigner-Ville spectrum of nonstationary processes, Berln, Vienna, New York:Springer-Verlag, 1984:15-23
[120]Jeong, J. and Williams, W. J., Kernel design for reduced interference distributions, IEEE Transactions on Signal Processing, 1992,40(2):402-412
[121]Choi, H.-I. and Williams, W. J., Improved time-frequency representation of multicomponent signals using exponential kernels, IEEE Transactions on Acoustics, Speech, and Signal Processing, 1989, 37(6):862-871
[122]Zhao, Y, Atlas, L. E. and Marks, R. J., Use of cone-shaped kernels for generalized time-frequency representations of nonstationary signals, IEEE Transactions on Acoustics, Speech, and Signal Processing, 1990,38(7): 1084-1091
[123]Auger, F. and Flandrin, P., Improving the readability of time-frequency and time-scale representations by the reassignment method, IEEE Transactions on Signal Processing, 1995,43(5): 1068-1089
[124]Cheung, S. and Lim, J. S., Combined multiresolution (wide-band/narrow-band) spectrogram, IEEE Transactions on Signal Processing, 1992,40(4):975-977
[125]Daniels, D. J., Ground Penetrating Radar, United Kingdom:The Institution of Electrical Engineers, 2004
[126]Van Dam, R. L., Borchers, B. and Hendrickx, J. M. H., Methods for prediction of soil dielectric properties: A review, Proceedings of SPIE, 2005, 5794:188-197
[127]Peplinski, N. R., Ulaby, F. T. and Dobson, M. C, Corrections to "Dielectric properties of soils in the 0.3-1.3-GHz range", IEEE Transactions on Geoscience and Remote Sensing, 1995, 33(6): 1340
[128]Peplinski, N. R., Ulaby, F. T. and Dobson, M. C, Dielectric properties of soils in the 0.3-1.3-GHz range, IEEE Transactions on Geoscience and Remote Sensing, 1995,33(3):803-807
[129]Blackham, D. V. and Pollard, R. D., An improved technique for permittivity measurements using a coaxial probe, IEEE Transactions on Instr. Meas., 1997, 46(50): 1093-1099
[130]Miller, T, Potter, L. and McCorkle, J., RFI suppression for ultra wideband radar, IEEE Transactions on Aerospace and Electronic Systems, 1997, 33(4): 1142-1156
[131]Buckreuss, S. and Horn, R., E-SAR P-band SAR subsystem design and RF-interference suppression, International Geoscience and Remote Sensing Symposium (IGARSS), Seattle, WA, USA, 1998:466-468
[132]Le, C. T. C., Hensley, S. and Chapin, E., Removal of RFI in wideband radars, International Geoscience and Remote Sensing Symposium (IGARSS), Seattle, WA, USA, 1998:2032-2034
[133]Lord, R. T. and Inggs, M. R., Approaches to RF interference suppression for VHF/UHF synthetic aperture radar, Proceedings of the South African Symposium on Communications and Signal Processing, 1998:95-100
[134]Lord, R. T. and Inggs, M. R., Efficient RFI suppression in SAR using a LMS adaptive filter with sidelobe suppression integrated with the range-Doppler algorithm, International Geoscience and Remote Sensing Symposium (IGARSS), 1999, 1:574-576
[135]Lord, R. T. and Inggs, M. R., Efficient RFI suppression in SAR using LMS adaptive filter integrated with range/Doppler algorithm, Electronics Letters, 1999, 35(8):629-630
[136]Luo, X., Ulander, L. M. H., Askne, J., Smith, G and Frolind, P. O., RFI suppression in ultra-wideband SAR systems using LMS filters in frequency domain, Electronics Letters, 2001, 37(4):241-243
[137]Reigber, A. and Ferro-Famil, L., Interference suppression in synthesized SAR images, IEEE Geoscience and Remote Sensing Letters, 2005, 2(1):45-49
[138]Lamont-Smith, T., Hill, R. D., Hayward, S. D., Yates, G. and Blake, A., Filtering approaches for interference suppression in low-frequency SAR, IEE Proceedings: Radar, Sonar and Navigation, 2006, 153(4):338-344
[139]袁孝康, 雷达图像的分辨特性, 系统工程与电子技术, 1999,21(3):26-30
[140]Brown, L. G., A survey of image registration techniques, ACM Computing Surveys, 1992, 24(4):325-376
[141]Zitova, B. and Flusser, J., Image registration methods: A survey, Image and Vision Computing, 2003, 21(11):977-1000
[142]Oliver, C. and Quegan, S., Understanding synthetic aperture radar imges, Boston, MA:Artech House, 1998
[143]Sun, Y. and Li, J., Landmine detection using forward-looking ground penetrating radar, Proceedings of SPIE, 2005, 5794:1089-1097
[144]Moreira,A.,An improved multi-look technique to produce SAR imagery,IEEE International Radar Conference,Arlington,VA,USA,1990:57-63
[145]Moreira,A.,Improved multilook techniques applied to SAR and SCANSAR imagery,IEEE Transactions on Geoscience and Remote Sensing,1991,29(4):529-534
[146]Wu,T.-K.,Radar cross section of arbitrarily shaped bodies of revolution,Proceedings of the IEEE,1989,77(5):735-740
[147]Wu,T.-K.and Tsai,L.L.,Scattering from arbitrarily-shaped lossy dielectric bodies of revolution,Radio Science,1977,12(5):709-718
[148]Yuceer,M.,Mautz,J.R.and Arvas,E.,Method of moments solution for the radar cross section of a chiral body of revolution,IEEE Transactions on Antennas and Propagation,2005,53(3):1163-1167
[149]黄培康等编著,雷达目标特性,北京:电子工业出版社,2005
[150]粟塔山等,最优化计算原理与算法程序设计,湖南长沙:国防科技大学出版社,2001
[151]Webb,A.R.,Statistical Pattern Recognition,Second Edition,England:John Wiley & Sons,Inc.,2002
[152]Sun,Y.and Li,J.,Adaptive learning approach to landmine detection,IEEE Transactions on Aerospace and Electronic Systems,2005,41(3):973-985
[153]Coohen,L.,Time-Frequency Analysis:Theory and Applications,Englewood Cliffs,NJ:Prentice Hall,1995
[154]Hu,M.K.,Visual pattem recognition by moment invariants,IRE Trans.Information Theory,1962,8(2):179-187
[155]Hupkens,T.M.and de Clippeleir,J.,Noise and intensity invariant moments,Pattern Recognition Letters,1995,16(4):371-376
[156]Gonzalez,R.C.and Woods,R.E.,Digital Image Processing,Second Edition,Englewood Cliffs,NJ:Prentice Hall,2002
[157]Burges,C.J.C.,A tutorial on support vector machines for pattern recognition,Data Mining and Knowledge Discovery,1998,2(2):121-167
[158]Cristianini,N.,Campbell,C.and Burges,C.,Editorial:Kernel methods:Current research and future directions,Machine Learning,2002,46(1-3):5-9
[159]Cristianini,N.and Shawe-Taylor,J.,An Introduction to Support Vector Machines and Other Kernel-based Learning Methods,Cambridge University Press,2000
[160]Meng,H.,Shawe-Taylor,J.,Szedmak,S.and Farquhar,J.D.R.,Support vector machine to synthesise kernels,Sheffield,United Kingdom,2005:242-255
[161]Schoelkopf,B.,Burges,C.and Vapnik,V.,Incorporating invariances in support vector learning machines,Bochum,Ger,1996:47
[162]Schoelkopf,B.,Sung,K.-K.,Burges,C.J.C.,Girosi,F.,Niyogi,P.,Poggio,T.and Vapnik,V.,Comparing support vector machines with Gaussian kernels to radial basis function classifiers,IEEE Transactions on Signal Processing,1997,45(11):2758-2765
[163]Vapnik,V.N.,Overview of statistical learning theory,IEEE Transactions on Neural Networks,1999,10(5):988-999
[164]吴高巍,统计学习理论及SVM算法研究,北京:中国科学院自动化研究所博士学位论文,2003,7
[165]Joachims,T.,Text categorization with support vector machines:learning with many relevant features,10th European Conference on Machine Learning, 1998:137-142
[166]Kandola, J., Hofmann, T., Poggio, T. and Shawe-Taylor, J., Introduction to the Special Issue on Machine Learning Methods for Text and Images, Journal of Machine Learning Research, 2003, 3(6): 1023-1024
[167]Bottou, L., Cortes, C., Denker, J. S., Drucker, H., Guyon, I., Jackel, L. D., LeCun, Y, Muller, U. A., Sackinger, E., Simard, P. and Vapnik, V., Comparison of classifier methods: A case study in handwritten digit recognition, Jerusalem, Isr, 1994:77-82
[168]LeCun, Y., Jackel, L. D., Bottou, L., Cortes, C., Denker, J. S., Drucker, H., Guyon, I., Muller, U. A., Sackinger, E., Simard, P. and Vapnik, V, Learning algorithms for classification: a comparison on handwritten digit recognition, Neural Networks: The statistical Mechanics Perspective 1995:261-276
[169]Kanevski, M., Pozdnukhov, A., Canu, S. and Maignan, M., Advanced spatial data analysis and modelling with support vector machines, International Journal on Fuzzy Systems, 2002:606-615
[170]Kanevski, M., Wong, P. and S., C., Spatial data mapping with support vector regression and geostatistics, 7th International Conference on Neural Information Processing, Taepon, Korea, 2000:1307-1311
[171]Jaakkola, T. S. and Haussler, D., Exploring generative models in discriminative classifiers, In Advances in Neural Information Processing Systems11, MIT press, 1999
[172]Carlson, D. W., Riddle, J. G. and Waagen, D. E., Selecting Training Images with Support Vector Machines for Composite Correlation Filters in SAR ATR, Proceedings of SPIE, 2003, 5095:292-302
[173]Krishnapuram, B., Sichina, J. and Carin, L., Physics-based detection of targets in SAR imagery using support vector machines, IEEE Sensors Journal, 2003, 3(2):147-157
[174] 《数学手册》编写组, 数学手册,北京:高等教育出版社, 1979
[175]Duda, R. O., Hart, P. E. and Stork, D. G., Pattern Classification, Second Edition, John Wiley & Sons, Inc., 2001
[176] Vapnik, V. N. and J., C. A., The necessary and sufficient conditions for consistency of the method of empirical risk minimization, Pattern Recognition and Image Analysis, 1991, 1(3):284-305
[177]Vapnik, V., The nature of statistical learning theory, New York:Springer-Verlag, 1995
[178] Vapnik, V. N., Book review: the nature of statistical learning theory, Technometrics, 1996, 38(4):400
[179]Kearns, M. and Shapire, R. E., Efficient distribution-free learning of probilistic concepts, The 31st Symposium on the Foundations of Computer Science, Los Alamitos, CA, 1990:382-391
[180]Shawe-Taylor, J., Bartlett, P. L., Williamson, R. C. and Anthony, M., Framework for structural risk minimization, The Annual ACM Conference on Computational Learning Theory, Desenzano del Garda, Italy, 1996:68-76
[181]Valiant, L. G., Theory of the learnable, Communications of the ACM, 1984, 27(11):1134-1142
[182]Chu, L. and Wu, C., A fuzzy support vector machine based on geometric model, Proceedings of the World Congress on Intelligent Control and Automation (WCICA),Hangzhou,China,2004:1843-1846
[183]Lin,C.-F.and Wang,S.-D.,Fuzzy support vector machines,IEEE Transactions on Neural Networks,2002,13(2):464-471
[184]Lin,C.-F.and Wang,S.-D.,Training algorithms for fuzzy support vector machines with noisy data,Pattern Recognition Letters,2004,25(14):1647-1656
[185]Dumais,S.T.,Platt,J.C.,Heckerman,D.and Sahami,M.,Inductive learning algorithms and representation for text categorization,7th International Conference on Informative and Knowledge Management,Betheda,Maryland,United States,1998:148-155
[186]Chapelle,O.,Haffner,P.and Vapnik,V.N.,Support vector machines for histogram-based image classification,IEEE Transactions on Neural Networks,1999,10(5):1055-1064
[187]Pontil,M.and Verri,A.,Support Vector Machines for 3D object recognition,IEEE Transactions on Pattern Analysis and Machine Intelligence,1998,20(6):637-646
[188]Scholkopf,B.,Support vector learning,Munich:R.Oldenbourg Verlag,1997
[189]Boser,B.E.,Guyon,I.M.and Vapinik,V.N.,A training algorithm for optimal margin classifiers,The 5th Annual ACM Workshop on Computational Learning Theory Pittsburgh,Pennsylvanin,United States,1992:144-152
[190]Cortes,C.and Vapnik,V.,Support-vector networks,Machine Learning,1995,20(3):273-297
[191]Cassabaum,M.,Waagen,D.,Rodriguez,J.J.and Schmitt,H.,Unsupervised optimization of support vector machine parameters,Proceedings of SPIE,2004,5426:316-325
[192]Waagen,D.,Cassabaum,M.,Schmitt,H.and Pollock,B.,Support vector machine optimization via margin distribution analysis,Proceedings of SPIE,2003,5094:348-357
[193]Kwok,J.T.-Y.,Evidence framework applied to support vector machines,IEEE Transactions on Neural Networks,2000,11(5):1162-1173
[194]MacKay,D.J.C.,Bayesian interpolation,Neural Computation,1992,4(3):415-447
[195]Rabiner,L.R.,A tutorial on hidden Markov models and selected applications in speech recognition,Proceeding of the IEEE,1989,77(2):257-286
[196]Shawe-Taylor,J.and Cristianini,N.,Kernel Methods for Pattern Analysis,England:Cambridge University Press,2004
[2]张直中,微波成像术,北京:科学出版社,1990
[3]张澄波,综合孔径雷达原理、系统分析与应用,北京:科学出版社,1989
[4]Farina,A.and Studer,F.A.,Detection with High Resolution Radar:Advanced Topics & potential Applications,Chinese Journal of Systems Engineering and Electronics,1992,3(1):21-34
[5]Fowler,C.,Entzminger,J.and Corum,J.,Assessment of ultra-wideband(UWB)technology,IEEE Aerospace and Electronic Systems Magazine,1990,5(11):45-49
[6]Taylor,J.D.,Ultra-wideband radar technology,New York:CRC press,2001
[7]Binder,B.T.,Toups,M.F.,Ayasli,S.and Adams,E.M.,SAR foliage penetration phenomenology of tropical rain forest and northern U.S.forest,Alexandria,VA,USA,1995:158-163
[8]Soumekh,M.,Nobles,D.A.,Wicks,M.C.and Genello,G.J.,Signal processing of wide bandwidth and wide beamwidth P-3 SAR data,IEEE Transactions on Aerospace and Electronic Systems,2001,37(4):1122-1141
[9]Toups,M.F.,Bessette,L.A.and Binder,B.T.,Foliage penetration data collections and investigations utilizing the P-3 UWB SAR,Proceedings of SPIE,1996,2757:136-144
[10]MacDonald,D.,Chang,C.-F.,Roman,J.and Koesel,R.,Automatic detection and cueing for foliage-concealed targets,Proceedings of SPIE,1996,2757:152-162
[11]Davis,M.E.,Tomlinson,P.G.and Maloney,R.P.,Technical challenges in ultra-wideband radar development for target detection and terrain mapping,IEEE National Radar Conference-Proceedings,1999:1-6
[12]http://www.landmine.org
[13]Gao,P.and Collins,L.M.,A two-dimensional generalized likelihood ratio test for land mine and small unexploded ordnance detection,Signal Processing,2000,80(8):1669-1686
[14]常文革,UWB SAR系统设计与实现,长沙:国防科学技术大学博士学位论文,2001,10
[15]祝明波,UWB-SAR信号设计与产生技术研究,长沙:国防科学技术大学博士学位论文,1999,10
[16]黎向阳,UWB-SAR接收技术研究,长沙:国防科学技术大学博士学位论文,2000,3
[17]王顺华,机载大处理角UWB-SAR成像理论及算法研究,长沙:国防科学技术大学博士学位论文,1998,10
[18]李肖东,基于相参子孔径机载SAR高分辨成像技术研究,长沙:国防科学技术大学博士学位论文,2003,10
[19]董臻,UWB-SAR信息处理中的若干问题研究,长沙:国防科学技术大学博士学位论文,2001,10
[20]刘光平,超宽带合成孔径雷达高效成像算法,长沙:国防科学技术大学博士学 位论文,2003,3
[21]方学立,UWB SAR图像中的目标检测与鉴别,长沙:国防科学技术大学博士学位论文,2005,10
[22]蒋咏梅,叶簇穿透超宽带SAR目标检测方法研究,长沙:国防科学技术大学博士学位论文,1998,10
[23]王岩,机载SAR目标特征提取与识别方法研究,长沙:国防科学技术大学博士学位论文,2003,10
[24]黄晓涛,UWB-SAR抑制RFI方法研究,长沙:国防科学技术大学博士学位论文,1999,11
[25]郭微光,机载超宽带合成孔径雷达运动补偿技术研究,长沙:国防科学技术大学博士学位论文,2003,4
[26]Carin,L.,Geng,N.,McClure,M.,Sichina,J.and Nguyen,L.,Ultra-wide-band synthetic-aperture radar for mine-field detection,IEEE Antennas and Propagation Magazine,1999,41(1):18-33
[27]DeLuca,C.,Marinelli,V.,Ressler,M.and Ton,T.,Unexploded ordnance detection experiments at extensive,fully ground-truthed test sites at Yuma Proving Ground and Eglin AFB,Proceedings of SPIE,1999,3710(Ⅱ):1025-1034
[28]Geng,N.,Carin,L.,Ressler,M.and Merchant,B.,Ultrawideband SAR for detection of subsurface unexploded ordnance(UXO):measurement,modeling and signal processing,Proceedings of SPIE,1999,3704:75-83
[29]Happ,L.,Kappra,K.A.,Ressler,M.A.,Sichina,J.P.,Sturgess,K.and Le,F.,Low-frequency ultra-wideband synthetic aperture radar 1995 BoomSAR tests,IEEE National Radar Conference,Ann Arbor,MI,USA,1996:54-59
[30]Happ,L.,Le,F.,Ressler,M.and Kappra,K.,Low-frequency ultrawideband synthetic aperture radar:frequency subbanding for targets obscured by the ground,Proceedings of SPIE,1996,2747:194-201
[31]Kaplan,L.M.,Oh,S.-M.and McClellan,J.H.,Detection of broadside targets during image formation using a quadtree approach,IEEE National Radar Conference,Alexandria,VA,USA,2000:104-109
[32]Kaplan,L.M.,Oh,S.-M.and McClellan,J.H.,Multiresolution target discrimination during image formation,Proceedings of SPIE,2000,4053:239-250
[33]Nguyen,L.,Kappra,K.,Wong,D.,Ressler,M.and Sichina,J.,Mine detection performance in different soil conditions using data from an ultra-wideband wide-area surveillance radar,Proceedings of SPIE,1999,3710(Ⅱ):930-941
[34]Ressler,M.,Merchant,B.,Wong,D.,Nguyen,L.,Geng,N.and Carin,L.,Calibration of the ARL BoomSAR using rigorous scattering models for fiducial targets over ground,Proceedings of SPIE,1999,3704:50-56
[35]Ressler,M.A.,Army research laboratory ultra wideband boomSAR,International Geoscience and Remote Sensing Symposium(IGARSS),Lincoln,NE,USA,1996:1886-1888
[36]Showman,G.A.and McClellan,J.H.,Blind polarimetric calibration of ultra-wideband SAR imagery,Proceedings of SPIE,2000,4033:102-113
[37]Sichina,J.,Nguyen,L.and Sullivan,A.,Using models and measurements to describe ultra wideband radar-scattering phenomena,Proceedings of SPIE,2000,4053:220-228
[38]Sullivan,A.,Damarla,T.R.,Geng,N.,Dong,Y.and Carin,L.,Detection of above ground and subsurface unexploded ordnance using ultra-wideband (UWB) synthetic aperture radar (SAR) and electromagnetic modeling tools, Proceedings of SPIE, 2000,4038 (II):983-992
[39] Sullivan, A., Geng, N., Carin, L., Nguyen, L. and Sichina, J., Performance analysis for radar detection of buried anti-tank and anti-personnel land mines, Proceedings of SPIE, 1999,3710(II): 1043-1050
[40] Ton, T. and Nguyen, L., Evaluation of the quadtree SAR image formation algorithm, Proceedings of SPIE, 2000,4041:78-85
[41] Wong, D., Ressler, M., Ton, T. and Merchant, B., UWB SAR calibration of mine imagery, Proceedings of SPIE, 1999,3704:57-65
[42] Vickers, R. S., Design and applications of airborne radars in the VHF/UHF band, IEEE Aerospace and Electronic Systems Magazine, 2002,17(6):26-29
[43] Kositsky, J., Results from a forward-looking GPR mine detection system, Proceedings of SPIE, 2000,4038 (II): 1077-1087
[44] Kositsky, J. and Amazeen, C, Results from a forward-looking GPR mine detection system, Proceedings of SPIE, 2001,4394:700-711
[45] Kositsky, J., Cosgrove, R., Amazeen, C. and Milanfar, P., Results from a forward-looking GPR mine detection system, Proceedings of SPIE, 2002, 4742:206-217
[46] Bradley, M., Witten, T., Duncan, M. and McCummins, B., Mine detection with a forward-looking ground-penetrating synthetic aperture radar, Proceedings of SPIE, 2003, 5089:334-347
[47] Bradley, M., Witten, T, Duncan, M. and McCummins, B., Anti-tank and side-attack mine detection with a forward-looking GPR, Proceedings of SPIE, 2004,5415:421-432
[48] Liu, G, Wang, Y, Li, J. and Bradley, M., SAR imaging for a forward-looking GPR system, Proceedings of SPIE, 2003, 5089:322-333
[49] http://www.miragegpr.com
[50] http://www.mineseeker.org
[51] Andrieu, J., Gallais, F., Mallepeyre, V., Bertrand, V., Beillard, B., Jecko, B., Guillerey, R. and Le Goff, M., Land mine detection with an ultra-wideband SAR system, Proceedings of SPIE, 2002, 4742:237-247
[52] Delmote, P., Dubois, C, Andrieu, J., Beillard, B., Lalande, M., Bertrand, V., Jecko, B., Pecastaing, L., Gibert, A., Paillol, J., Domens, P., Guillerey, R., Monnier, F. and Legoff, M., The UWB SAR system Pulsar: New generator and antenna developments, Proceedings of SPIE, 2003, 5077:223-234
[53] Homer, J., Tang, H. T. and Longstaff, I. D., Radar imaging of shallow buried objects, International Geoscience and Remote Sensing Symposium (IGARSS), Hamburg, Ger, 1999:2477-2479
[54] Lloyd, D. and Longstaff, I. D., Ultra-wideband multistatic SAR for the detection and location of landmines, IEE Proceedings: Radar, Sonar and Navigation, 2003, 150(3):158-164
[55] Stickley, G. F., Noon, D. A., Cherniakov, M. and Longstaff, I. D., Preliminary field results of an ultra-wideband (10-620 MHz) stepped-frequency ground penetrating radar, International Geoscience and Remote Sensing Symposium (IGARSS), Singapore, 1997:1282-1284
[56] Wang, Y, Longstaff, I. D. and Leat, C. J., SAR. imaging of buried objects from MoM modelled scattered field, IEE Proceedings: Radar, Sonar and Navigation, 2001,148(3):167-172
[57]Bamler,R.,A comparison of range-Doppler and wavenumber domain SAR focusing algorithms,IEEE Transactions on Geoscience and Remote Sensing,1992,30(4):706-713
[58]Cafforio,C.,Prati,C.and Rocca,F.,SAR data focusing using seismic migration techniques,IEEE Transactions on Aerospace and Electronic Systems,1991,27(2):194-207
[59]Prati,C.,Rocca,F.,Guarnieri,A.M.and Damonti,E.,Seismic migration for SAR focusing:Interferometrical applications,IEEE Transactions on Geoscience and Remote Sensing,1990,28(4):627-640
[60]Soumekh,M.,Synthetic aperture radar signal processing with MATLAB algorithm,New York:John Wiley & Sons,INC,1999
[61]Barber,B.C.,Theory of digital imaging from orbital synthetic-aperture radar,International Journal of Remote Sensing,1985,6(7):1009-1057
[62]Hellsten,H.,Ulander,L.M.,Gustavsson,A.and Larsson,B.,Development of VHF CARABAS Ⅱ SAR,Proceedings of SPIE,1996,2747:48-60
[63]McCorkle,J.W.and Rofheart,M.,An order N21og(N) backprojector algorithm for focusing wide-angle wide-bandwidth arbitrary-motion synthetic aperture radar,Proceedings of SPIE,1996,2747:25-36
[64]Ulander,L.M.H.,Hellsten,H.and Stenstrom,G.,Synthetic-aperture radar processing using fast factorized back-projection,IEEE Transactions on Aerospace and Electronic Systems,2003,39(3):760-776
[65]DiPietro,R.C.,Fante,R.L.,Perry,R.P.,Soumekh,M.and Tromp,L.D.,Multi-resolution FOPEN SAR image formation,Proceedings of SPIE,1999,3721:170-188
[66]Wang,Y.,Li,X.,Sun,Y.,Li,J.and Stoica,P.,Adaptive imaging for forward-looking ground penetrating radar,IEEE Transactions on Aerospace and Electronic Systems,2005,41(3):922-936
[67]Cai,L.and Walton,E.,SAR imaging through complex media,21 st annual meeting and symposium of Antenna Measurement Techniques Association,Monterey Bay,California,1999:131-134
[68]Fortuny-Guasch,J.,A novel 3-D subsurface radar imaging technique,IEEE Transactions on Geoscience and Remote Sensing,2002,40(2):443-452
[69]Groenenboom,J.and Yarovoy,A.G,Data processing for landmine detection dedicated GPR,Proceedings of SPIE,2000,4084:867-871
[70]Milisavljevic,N.and Yarovoy,A.G.,An effective algorithm for subsurface SAR imaging,AP-S International Symposium,San Antonio,TX,2002:314-317
[71]Gu,K.,Wang,G.and Li,J.,Migration based SAR imaging for ground penetrating radar systems,lEE Proceedings:Radar,Sonar and Navigation,2004,151(5):317-325
[72]Gough,P.T.and Hunt,B.R.,Synthetic aperture radar image reconstruction algorithms designed for subsurface imaging,International Geoscience and Remote Sensing Symposium(IGARSS),Singapore,1997:1588-1590
[73]Novak,L.M.,Halversen,S.D.,Owirka,G.J.and Hiett,M.,Effects of polarization and resolution on SAR ATR,IEEE Transactions on Aerospace and Electronic Systems,1997,33(1):102-116
[74]Geng,N.and Carin,L.,Resonances of a dielectric BOR buried in a lossy,dispersive layered medium,International Geoscience and Remote Sensing Symposium (IGARSS), Seattle, WA, USA, 1998:222-224
[75] Geng, N., Jackson, D. R. and Carin, L., On the resonances of a dielectric BOR buried in a dispersive layered medium, IEEE Transactions on Antennas and Propagation, 1999,47(8):1305-1313
[76] He, J., Yu, T., Geng, N. and Carin, L., Method of moments analysis of electromagnetic scattering from a general three-dimensional dielectric target embedded in a multilayered medium, Radio Science, 2000, 35(2):305-313
[77] Nguyen, L., Kapoor, R., Wong, D. and Sichina, J., Ultra-wideband radar target discrimination utilizing an advanced feature set, Proceedings of SPIE, 1998, 3370:289-306
[78] Nguyen, L., Kappra, K., Wong, D., Kapoor, R. and Sichina, J., A mine field detection algorithm utilizing data from an ultra-wideband wide-area surveillance radar, Proceedings of SPIE, 1998,3392:627-643
[79] Vitebskiy, S., Carin, L., Ressler, M. A. and Le, F. H., Ultra-wideband, short-pulse ground-penetrating radar: Simulation and measurement, IEEE Transactions on Geoscience and Remote Sensing, 1997, 35(3):762-772
[80] Casey, K. F. and Oetzel, G. N., Physical-optics models for scatterng from bured mines, Second Australian-American Joint Conference on the Technology of Mine Countermeasures, Sydney, Australia, 2001:1 -15
[81] Cosgrove, R. B., Milanfar, P. and Kositsky, J., Trained detection of buried mines in SAR images via the deflection-optimal criterion, IEEE Transactions on Geoscience and Remote Sensing, 2004,42(11):2569-2575
[82] Allen, M. R., Efficient approach to physics-based FOPEN-SAR ATD/R, Proceedings of SPIE, 1996,2757:163-172
[83] Allen, M. R., Jauregui, J. M. and Hoff, L. E., FOPEN-SAR detection by direct use of simple scattering physics, IEEE National Radar Conference, Alexandria, VA, USA, 1995:152-157
[84] Allen, M. R., Phillips, S. A. and Sofianos, D. J., Wide-angle SAR-matched filter image formation for enhanced detection performance, Proceedings of SPIE, 1994, 2093:381-387
[85] Chaney, R. D., Willsky, A. S. and Novak, L. M., Coherent aspect-dependent SAR image formation, Proceedings of SPIE, 1994, 2230:256-274
[86] Dong, Y, Runkle, P. R., Carin, L., Damarla, R., Sullivan, A., Ressler, M. A. and Sichina, J., Multi-aspect detection of surface and shallow-buried unexploded ordnance via ultra-wideband synthetic aperture radar, IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(6):1259-1270
[87] Carin, L., Geng, N., McClure, M., Dong, Y, Liu, Z., He, J., Sichina, J., Ressler, M., Nguyen, L. and Sullivan, A., Wide-area detection of land mines and unexploded ordnance, Inverse Problems, 2002,18(3):575-609
[88] Damarla, T. R., A Fast Algorithm for Computing Scattered Fields Using Physical Optics Equivalent Approximation in Half-Space, Army Research Laboratory Report, July 2000
[89] Tax, D., One-class classification: concept-learning in the absence of counter-examples, Deflt University of Technology, PhD thesis, 2001, June
[90] Soumekh, M., Reconnaissance with ultra wideband UHF synthetic aperture radar, IEEE Signal Processing Magazine, 1995,12(4):21-40
[91] Eaves, J. L. and Reedy, E. K., Principles of Moden Radar, New York:Van Nostrand Reinhold, 1987
[92] Kaplan, L. M, McClellan, J. H. and Oh, S.-M., Prescreening during image formation for ultrawideband radar, IEEE Transactions on Aerospace and Electronic Systems, 2002, 38(1):74-88
[93] Carin, L., Ybarra, G., Bharadwaj, P. and Runkle, P., Physics-based classification of targets in SAR imagery using subaperture sequences, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Phoenix, AZ, USA, 1999:3341-3344
[94] Runkle, P., Carin, L. and Nguyen, L., Multi-aspect target classification using hidden Markov models for data fusion, International Geoscience and Remote Sensing Symposium (IGARSS), Honolulu, HI, USA, 2000:2123-2125
[95] Runkle, P., Nguyen, L. and Carin, L., Multi-aspect target detection for SAR imagery using hidden Markov models and two-dimensional matching pursuits, Proceedings of SPIE, 2000,4053:251-259
[96] Runkle, P., Nguyen, L., Ybarra, G. and Carin, L., Multi-aspect target detection in SAR imagery using hidden Markov models, Proceedings of SPIE, 1999, 3721:214-223
[97] Runkle, P., Nguyen, L. H., McClellan, J. H. and Carin, L., Multi-aspect target detection for SAR imagery using hidden Markov models, IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(1):46-55
[98] Soumekh, M., Gunther, G., Linderman, M. and Kohler, R., Digitally-spotlighted subaperture SAR image formation using high performance computing, Proceedings of SPIE, 2000,4053:260-271
[99] Wong, D. and Carin, L., Analysis and processing of ultra wide-band SAR imagery for buried landmine detection, IEEE Transactions on Antennas and Propagation, 1998, 46(11):1747-1748
[100]Astanin, L. Y. and Kostylev, A. A., Utrawideband Radar Measurements Analysis and Processing, London, United Kingdom:The institution of Electrival Engineers, 1997
[101] 保铮等,雷达成像技术, 北京:电子工业出版社,2005
[102]Jakowatz, C. V. J., Wahl, D. E., Eichel, P. H., Ghiglia, D. C. and Thompson, P. A., Spotlight-Mode Synthetic Aperture Radar: A signal Processing Approach, Spinger, 1996
[103]Goodman, R., Tummala, S. and Carrara, W., Issues in ultra-wideband, widebeam SAR image formation, IEEE National Radar Conference, Alexandria, VA, USA, 1995:479-485
[104]王亮等, UWB SAR 成像中非正交旁瓣的抑制, 信号处理, 2006, 22(1):28-31
[105]McCorkle, J. W., Focusing of synthetic aperture ultra wideband data, International Geoscience and Remote Sensing Symposium (IGARSS), Fairborn, OH, USA, 1991:1-5
[106]Rau, R. and McClellan, J. H., Analytic models and postprocessing techniques for UWB SAR, IEEE Transactions on Aerospace and Electronic Systems, 2000, 36(4): 1058-1074
[107]Carrara, W. G, Goodman, R. S. and Majewski, R. M., Spotlight synthetic aperture radar: signal processing algorithms, Boston:Artech House, 1995
[108]Moreira, J. R., A new method of aircraft motion error extraction from radar raw data for real-time motion compensation, IEEE Transactions on Geoscience and Remote Sensing, 1990, 28(4):620-626
[109]Wahl, D. E., Eichel, P. H., Ghiglia, D. C. and Jakowatz, C. V., Jr., Phase gradient autofocus - a robust tool for high resolution SAR phase correction, IEEE Transactions on Aerospace and Electronic Systems, 1994, 30(3):827-835
[110]Thompson, D. G, Bates, J. S., Arnold, D. V., Long, D. G and Robertson, A., Range dependent phase gradient autofocus, International Geoscience and Remote Sensing Symposium (IGARSS), Seattle, WA, USA, 1998:2634-2636
[111]Potsis, A., Reigber, A., Mittermayer, J., Moreira, A. and Uzunoglou, N., Sub-aperture algorithm for motion compensation improvement in wide-beam SAR data processing, Electronics Letters, 2001, 37(23): 1405-1407
[112] Reigber, A., Potsis, A., Alivizatos, E., Uzunoglu, N. and Moreira, A., Wavenumber Domain SAR Focusing with Integrated Motion Compensation, International Geoscience and Remote Sensing Symposium (IGARSS), Toulouse, France, 2003:1465-1467
[113]Thayaparan, T., Lampropoulos, G., Wong, S. K. and Riseborough, E., Application of adaptive joint time-frequency algorithm for focusing distorted ISAR images from simulated and measured radar data, IEE Proceedings: Radar, Sonar and Navigation, 2003,150(4):213-220
[114]Wong, S. K., Duff, G. and Riseborough, E., Distortion in the inverse synthetic aperture radar (ISAR) images of a target with time-varying perturbed motion, IEE Proceedings: Radar, Sonar and Navigation, 2003,150(4):221-227
[115]Li, J. and Ling, H., Application of adaptive chirplet representation for ISAR feature extraction from targets with rotating parts, IEE Proceedings: Radar, Sonar and Navigation, 2003,150(4):284-291
[116]Choi, I. S., Cho, B. L. and Kim, H. T., ISAR motion compensation using evolutionary adaptive wavelet transform, IEE Proceedings: Radar, Sonar and Navigation, 2003,150(4):229-233
[117]Ovarlez, J. P., Vignaud, L., Castelli, J. C., Tria, M. and Benidir, M., Analysis of SAR images by multidimensional wavelet transform, IEE Proceedings: Radar, Sonar and Navigation, 2003,150(4):234-241
[118]Vignaud, L., Wavelet-RELAX feature extraction in radar images, IEE Proceedings: Radar, Sonar and Navigation, 2003,150(4):242-246
[119]Flandrin, P. and Martin, W., A general class of estimators for the Wigner-Ville spectrum of nonstationary processes, Berln, Vienna, New York:Springer-Verlag, 1984:15-23
[120]Jeong, J. and Williams, W. J., Kernel design for reduced interference distributions, IEEE Transactions on Signal Processing, 1992,40(2):402-412
[121]Choi, H.-I. and Williams, W. J., Improved time-frequency representation of multicomponent signals using exponential kernels, IEEE Transactions on Acoustics, Speech, and Signal Processing, 1989, 37(6):862-871
[122]Zhao, Y, Atlas, L. E. and Marks, R. J., Use of cone-shaped kernels for generalized time-frequency representations of nonstationary signals, IEEE Transactions on Acoustics, Speech, and Signal Processing, 1990,38(7): 1084-1091
[123]Auger, F. and Flandrin, P., Improving the readability of time-frequency and time-scale representations by the reassignment method, IEEE Transactions on Signal Processing, 1995,43(5): 1068-1089
[124]Cheung, S. and Lim, J. S., Combined multiresolution (wide-band/narrow-band) spectrogram, IEEE Transactions on Signal Processing, 1992,40(4):975-977
[125]Daniels, D. J., Ground Penetrating Radar, United Kingdom:The Institution of Electrical Engineers, 2004
[126]Van Dam, R. L., Borchers, B. and Hendrickx, J. M. H., Methods for prediction of soil dielectric properties: A review, Proceedings of SPIE, 2005, 5794:188-197
[127]Peplinski, N. R., Ulaby, F. T. and Dobson, M. C, Corrections to "Dielectric properties of soils in the 0.3-1.3-GHz range", IEEE Transactions on Geoscience and Remote Sensing, 1995, 33(6): 1340
[128]Peplinski, N. R., Ulaby, F. T. and Dobson, M. C, Dielectric properties of soils in the 0.3-1.3-GHz range, IEEE Transactions on Geoscience and Remote Sensing, 1995,33(3):803-807
[129]Blackham, D. V. and Pollard, R. D., An improved technique for permittivity measurements using a coaxial probe, IEEE Transactions on Instr. Meas., 1997, 46(50): 1093-1099
[130]Miller, T, Potter, L. and McCorkle, J., RFI suppression for ultra wideband radar, IEEE Transactions on Aerospace and Electronic Systems, 1997, 33(4): 1142-1156
[131]Buckreuss, S. and Horn, R., E-SAR P-band SAR subsystem design and RF-interference suppression, International Geoscience and Remote Sensing Symposium (IGARSS), Seattle, WA, USA, 1998:466-468
[132]Le, C. T. C., Hensley, S. and Chapin, E., Removal of RFI in wideband radars, International Geoscience and Remote Sensing Symposium (IGARSS), Seattle, WA, USA, 1998:2032-2034
[133]Lord, R. T. and Inggs, M. R., Approaches to RF interference suppression for VHF/UHF synthetic aperture radar, Proceedings of the South African Symposium on Communications and Signal Processing, 1998:95-100
[134]Lord, R. T. and Inggs, M. R., Efficient RFI suppression in SAR using a LMS adaptive filter with sidelobe suppression integrated with the range-Doppler algorithm, International Geoscience and Remote Sensing Symposium (IGARSS), 1999, 1:574-576
[135]Lord, R. T. and Inggs, M. R., Efficient RFI suppression in SAR using LMS adaptive filter integrated with range/Doppler algorithm, Electronics Letters, 1999, 35(8):629-630
[136]Luo, X., Ulander, L. M. H., Askne, J., Smith, G and Frolind, P. O., RFI suppression in ultra-wideband SAR systems using LMS filters in frequency domain, Electronics Letters, 2001, 37(4):241-243
[137]Reigber, A. and Ferro-Famil, L., Interference suppression in synthesized SAR images, IEEE Geoscience and Remote Sensing Letters, 2005, 2(1):45-49
[138]Lamont-Smith, T., Hill, R. D., Hayward, S. D., Yates, G. and Blake, A., Filtering approaches for interference suppression in low-frequency SAR, IEE Proceedings: Radar, Sonar and Navigation, 2006, 153(4):338-344
[139]袁孝康, 雷达图像的分辨特性, 系统工程与电子技术, 1999,21(3):26-30
[140]Brown, L. G., A survey of image registration techniques, ACM Computing Surveys, 1992, 24(4):325-376
[141]Zitova, B. and Flusser, J., Image registration methods: A survey, Image and Vision Computing, 2003, 21(11):977-1000
[142]Oliver, C. and Quegan, S., Understanding synthetic aperture radar imges, Boston, MA:Artech House, 1998
[143]Sun, Y. and Li, J., Landmine detection using forward-looking ground penetrating radar, Proceedings of SPIE, 2005, 5794:1089-1097
[144]Moreira,A.,An improved multi-look technique to produce SAR imagery,IEEE International Radar Conference,Arlington,VA,USA,1990:57-63
[145]Moreira,A.,Improved multilook techniques applied to SAR and SCANSAR imagery,IEEE Transactions on Geoscience and Remote Sensing,1991,29(4):529-534
[146]Wu,T.-K.,Radar cross section of arbitrarily shaped bodies of revolution,Proceedings of the IEEE,1989,77(5):735-740
[147]Wu,T.-K.and Tsai,L.L.,Scattering from arbitrarily-shaped lossy dielectric bodies of revolution,Radio Science,1977,12(5):709-718
[148]Yuceer,M.,Mautz,J.R.and Arvas,E.,Method of moments solution for the radar cross section of a chiral body of revolution,IEEE Transactions on Antennas and Propagation,2005,53(3):1163-1167
[149]黄培康等编著,雷达目标特性,北京:电子工业出版社,2005
[150]粟塔山等,最优化计算原理与算法程序设计,湖南长沙:国防科技大学出版社,2001
[151]Webb,A.R.,Statistical Pattern Recognition,Second Edition,England:John Wiley & Sons,Inc.,2002
[152]Sun,Y.and Li,J.,Adaptive learning approach to landmine detection,IEEE Transactions on Aerospace and Electronic Systems,2005,41(3):973-985
[153]Coohen,L.,Time-Frequency Analysis:Theory and Applications,Englewood Cliffs,NJ:Prentice Hall,1995
[154]Hu,M.K.,Visual pattem recognition by moment invariants,IRE Trans.Information Theory,1962,8(2):179-187
[155]Hupkens,T.M.and de Clippeleir,J.,Noise and intensity invariant moments,Pattern Recognition Letters,1995,16(4):371-376
[156]Gonzalez,R.C.and Woods,R.E.,Digital Image Processing,Second Edition,Englewood Cliffs,NJ:Prentice Hall,2002
[157]Burges,C.J.C.,A tutorial on support vector machines for pattern recognition,Data Mining and Knowledge Discovery,1998,2(2):121-167
[158]Cristianini,N.,Campbell,C.and Burges,C.,Editorial:Kernel methods:Current research and future directions,Machine Learning,2002,46(1-3):5-9
[159]Cristianini,N.and Shawe-Taylor,J.,An Introduction to Support Vector Machines and Other Kernel-based Learning Methods,Cambridge University Press,2000
[160]Meng,H.,Shawe-Taylor,J.,Szedmak,S.and Farquhar,J.D.R.,Support vector machine to synthesise kernels,Sheffield,United Kingdom,2005:242-255
[161]Schoelkopf,B.,Burges,C.and Vapnik,V.,Incorporating invariances in support vector learning machines,Bochum,Ger,1996:47
[162]Schoelkopf,B.,Sung,K.-K.,Burges,C.J.C.,Girosi,F.,Niyogi,P.,Poggio,T.and Vapnik,V.,Comparing support vector machines with Gaussian kernels to radial basis function classifiers,IEEE Transactions on Signal Processing,1997,45(11):2758-2765
[163]Vapnik,V.N.,Overview of statistical learning theory,IEEE Transactions on Neural Networks,1999,10(5):988-999
[164]吴高巍,统计学习理论及SVM算法研究,北京:中国科学院自动化研究所博士学位论文,2003,7
[165]Joachims,T.,Text categorization with support vector machines:learning with many relevant features,10th European Conference on Machine Learning, 1998:137-142
[166]Kandola, J., Hofmann, T., Poggio, T. and Shawe-Taylor, J., Introduction to the Special Issue on Machine Learning Methods for Text and Images, Journal of Machine Learning Research, 2003, 3(6): 1023-1024
[167]Bottou, L., Cortes, C., Denker, J. S., Drucker, H., Guyon, I., Jackel, L. D., LeCun, Y, Muller, U. A., Sackinger, E., Simard, P. and Vapnik, V., Comparison of classifier methods: A case study in handwritten digit recognition, Jerusalem, Isr, 1994:77-82
[168]LeCun, Y., Jackel, L. D., Bottou, L., Cortes, C., Denker, J. S., Drucker, H., Guyon, I., Muller, U. A., Sackinger, E., Simard, P. and Vapnik, V, Learning algorithms for classification: a comparison on handwritten digit recognition, Neural Networks: The statistical Mechanics Perspective 1995:261-276
[169]Kanevski, M., Pozdnukhov, A., Canu, S. and Maignan, M., Advanced spatial data analysis and modelling with support vector machines, International Journal on Fuzzy Systems, 2002:606-615
[170]Kanevski, M., Wong, P. and S., C., Spatial data mapping with support vector regression and geostatistics, 7th International Conference on Neural Information Processing, Taepon, Korea, 2000:1307-1311
[171]Jaakkola, T. S. and Haussler, D., Exploring generative models in discriminative classifiers, In Advances in Neural Information Processing Systems11, MIT press, 1999
[172]Carlson, D. W., Riddle, J. G. and Waagen, D. E., Selecting Training Images with Support Vector Machines for Composite Correlation Filters in SAR ATR, Proceedings of SPIE, 2003, 5095:292-302
[173]Krishnapuram, B., Sichina, J. and Carin, L., Physics-based detection of targets in SAR imagery using support vector machines, IEEE Sensors Journal, 2003, 3(2):147-157
[174] 《数学手册》编写组, 数学手册,北京:高等教育出版社, 1979
[175]Duda, R. O., Hart, P. E. and Stork, D. G., Pattern Classification, Second Edition, John Wiley & Sons, Inc., 2001
[176] Vapnik, V. N. and J., C. A., The necessary and sufficient conditions for consistency of the method of empirical risk minimization, Pattern Recognition and Image Analysis, 1991, 1(3):284-305
[177]Vapnik, V., The nature of statistical learning theory, New York:Springer-Verlag, 1995
[178] Vapnik, V. N., Book review: the nature of statistical learning theory, Technometrics, 1996, 38(4):400
[179]Kearns, M. and Shapire, R. E., Efficient distribution-free learning of probilistic concepts, The 31st Symposium on the Foundations of Computer Science, Los Alamitos, CA, 1990:382-391
[180]Shawe-Taylor, J., Bartlett, P. L., Williamson, R. C. and Anthony, M., Framework for structural risk minimization, The Annual ACM Conference on Computational Learning Theory, Desenzano del Garda, Italy, 1996:68-76
[181]Valiant, L. G., Theory of the learnable, Communications of the ACM, 1984, 27(11):1134-1142
[182]Chu, L. and Wu, C., A fuzzy support vector machine based on geometric model, Proceedings of the World Congress on Intelligent Control and Automation (WCICA),Hangzhou,China,2004:1843-1846
[183]Lin,C.-F.and Wang,S.-D.,Fuzzy support vector machines,IEEE Transactions on Neural Networks,2002,13(2):464-471
[184]Lin,C.-F.and Wang,S.-D.,Training algorithms for fuzzy support vector machines with noisy data,Pattern Recognition Letters,2004,25(14):1647-1656
[185]Dumais,S.T.,Platt,J.C.,Heckerman,D.and Sahami,M.,Inductive learning algorithms and representation for text categorization,7th International Conference on Informative and Knowledge Management,Betheda,Maryland,United States,1998:148-155
[186]Chapelle,O.,Haffner,P.and Vapnik,V.N.,Support vector machines for histogram-based image classification,IEEE Transactions on Neural Networks,1999,10(5):1055-1064
[187]Pontil,M.and Verri,A.,Support Vector Machines for 3D object recognition,IEEE Transactions on Pattern Analysis and Machine Intelligence,1998,20(6):637-646
[188]Scholkopf,B.,Support vector learning,Munich:R.Oldenbourg Verlag,1997
[189]Boser,B.E.,Guyon,I.M.and Vapinik,V.N.,A training algorithm for optimal margin classifiers,The 5th Annual ACM Workshop on Computational Learning Theory Pittsburgh,Pennsylvanin,United States,1992:144-152
[190]Cortes,C.and Vapnik,V.,Support-vector networks,Machine Learning,1995,20(3):273-297
[191]Cassabaum,M.,Waagen,D.,Rodriguez,J.J.and Schmitt,H.,Unsupervised optimization of support vector machine parameters,Proceedings of SPIE,2004,5426:316-325
[192]Waagen,D.,Cassabaum,M.,Schmitt,H.and Pollock,B.,Support vector machine optimization via margin distribution analysis,Proceedings of SPIE,2003,5094:348-357
[193]Kwok,J.T.-Y.,Evidence framework applied to support vector machines,IEEE Transactions on Neural Networks,2000,11(5):1162-1173
[194]MacKay,D.J.C.,Bayesian interpolation,Neural Computation,1992,4(3):415-447
[195]Rabiner,L.R.,A tutorial on hidden Markov models and selected applications in speech recognition,Proceeding of the IEEE,1989,77(2):257-286
[196]Shawe-Taylor,J.and Cristianini,N.,Kernel Methods for Pattern Analysis,England:Cambridge University Press,2004