声透镜成像关键技术研究
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摘要
本文围绕着声透镜成像技术，对透镜声场模型的改进、透镜系统的分析与设计、透镜系统的水池试验及可用于声透镜成像技术的压缩传感理论四个方面展开了研究。
详细阐述了混合法透镜声场模型的基本结构及实施方法，并分析了其优点及仍需改进的地方。在此基础上，提出了改进的混合法透镜声场模型，即改进的射线声学-基尔霍数值积分混合模型和改进的射线声学-求和法混合模型。详细介绍了改进的混合法的基本结构及实现方法，并给出了仿真与试验结果，从而验证了改进的混合法透镜声场模型较原混合模型在模拟透镜声场性能上具有更高的准确度。
运用改进的混合法透镜声场模型，仿真分析了透镜系统的焦点布放问题，详细研究了透镜系统的各参数对透镜系统波束性能的影响情况。这些参数包括几何参数（界面形状、界面尺寸、透镜个数及镜间距）、材料参数（声速和声衰减系数）、环境温度和工作频率等。根据仿真分析得到的先验知识，进行了四个透镜系统的设计：一、由单个透镜构成的单透镜系统；二、由两个透镜构成的透镜组系统；三、在水平方向和垂直方向上都具有聚焦性能的二维透镜；四、能够抵抗环境温度影响复合透镜系统，并给出了仿真结果及加工成型的透镜实物图。
对已设计的透镜系统进行了水池试验，试验共分为两部分，第一部分为透镜系统的波束形成试验，试验内容包括透镜系统焦点位置的确定，波束指向性图的绘制，及聚焦深度的测量，试验结果与仿真结果基本吻合，充分验证了论文提出的改进的混合法用于模拟透镜声场的准确性。第二部分为透镜声纳样机系统的目标成像试验，利用单透镜声纳样机系统和透镜组声纳样机系统分别对圆环和叉形两种目标进行了成像试验，所成的目标图像轮廓清晰，更新速度快，从而充分地说明了透镜声纳用于目标成像的可行性及其良好的性能。
阐述了压缩传感理论在声透镜成像技术中的应用前景，详细论述了压缩传感三个方面的基本理论：信号的稀疏表示、测量值的获取和信号的重构，重点研究了匹配追踪系列的信号重构算法，在正则化正交匹配追踪算法的基础上提出了一种渐进式的正则化自适应匹配追踪算法，仿真研究表明，用所提算法重建的图像在峰值信噪比、相对误差、匹配度及运行时间相比于现有相关算法都有提高。深入研究了分块压缩传感理论，在图像自适应分块压缩传感重建模型的基础之上，根据水声图像的特点，提出了一种新的图像自适应分块压缩传感重建算法，所提算法消除了图像中目标区域相邻块之间的块效应，提高了图像的重建质量，方便后续的目标特征提取及识别。
The thesis discussed the acoustic lens imaging technology. Four main contents wereincluded in the thesis: improving the model of sound field generated by acoustic lens;analyzing and designing acoustic lens system; testing in a tank; compressive sening theorythan can be applied in acoustic lens imaging technology.
The hybrid geometric/wave acoustic model was introduced in detail, and that theadvantages and disadvantages were analyzed. The improved hybrid geometric/wave acousticmodels were proposed. Detailed structure and implementation steps of improved model weregiven. Experimental results were compared to simulation. Therefrom, the improved modelhad more accuracy than the original hybrid model.
The performance of an acoustic lens system was analyzed using the improved hybridmodel, such as focal region and parameter’s influnce on beampatterns of acoustic lens. Theparameters of lens system were geometry、material、tempreture、frequency and so on. Basedon the simulation results, four acoustic lens systems were designed: single-element acousticlens system; two-element acoustic lens system; two-demension acoustic lens; compoundacoustic lens system. The simulation and picture of the designed acoustic lens system weregiven.
The tank tests were operated for the designed acoustic lens system. The test were dividedinto two parts. One was the beamforming test of the acoustic lens system, the other wasbeamforming and imaging test of the prototype. The former concluded testing the focalposition, drawing the beampatterns, measuring the field of view and practical range. Thecomparison between the simulation and test results verified the good accuracy of theimproved hybrid model in modeling the sound field. The latter concluded testing thebeamwidth of the prototype in the horizontal axis, testing the fied-of-view in the horizontaland vertical axis, testing the pratical range and range resolution and imaging the objects withshape of circle and cross. The images formed by the prototype had clearcutness and rapidupdate rate. Therefor, acoustic lens system were available in underwater acoustic imaging.
Compressed sensing theory had potential application in acoustic lens image technology.Three aspects of compressed sensing theory: sparse representation, acquring measured dataand reconstruction of signal were introduced in detail.Orthogonal matching pursuit series theory was researched implemented. A gradual regularized adaptive matching pursuit was presented. Simulation results proved that theproposed algorithm improved peak signal noise ratio、relative error、matching and runtimeof reconstructed image. Block compressed sensing theory was the other research focus.According to the characteristics of underwater acoustic image, a new adaptive blockcompressed sensing reconstructing scheme was proposed. The improving algorithmeliminated blocking effect between neighboring subblocks in target area. The quality ofreconstructed image was increased which was convenient for feature extraction and targetidentification.
详细阐述了混合法透镜声场模型的基本结构及实施方法，并分析了其优点及仍需改进的地方。在此基础上，提出了改进的混合法透镜声场模型，即改进的射线声学-基尔霍数值积分混合模型和改进的射线声学-求和法混合模型。详细介绍了改进的混合法的基本结构及实现方法，并给出了仿真与试验结果，从而验证了改进的混合法透镜声场模型较原混合模型在模拟透镜声场性能上具有更高的准确度。
运用改进的混合法透镜声场模型，仿真分析了透镜系统的焦点布放问题，详细研究了透镜系统的各参数对透镜系统波束性能的影响情况。这些参数包括几何参数（界面形状、界面尺寸、透镜个数及镜间距）、材料参数（声速和声衰减系数）、环境温度和工作频率等。根据仿真分析得到的先验知识，进行了四个透镜系统的设计：一、由单个透镜构成的单透镜系统；二、由两个透镜构成的透镜组系统；三、在水平方向和垂直方向上都具有聚焦性能的二维透镜；四、能够抵抗环境温度影响复合透镜系统，并给出了仿真结果及加工成型的透镜实物图。
对已设计的透镜系统进行了水池试验，试验共分为两部分，第一部分为透镜系统的波束形成试验，试验内容包括透镜系统焦点位置的确定，波束指向性图的绘制，及聚焦深度的测量，试验结果与仿真结果基本吻合，充分验证了论文提出的改进的混合法用于模拟透镜声场的准确性。第二部分为透镜声纳样机系统的目标成像试验，利用单透镜声纳样机系统和透镜组声纳样机系统分别对圆环和叉形两种目标进行了成像试验，所成的目标图像轮廓清晰，更新速度快，从而充分地说明了透镜声纳用于目标成像的可行性及其良好的性能。
阐述了压缩传感理论在声透镜成像技术中的应用前景，详细论述了压缩传感三个方面的基本理论：信号的稀疏表示、测量值的获取和信号的重构，重点研究了匹配追踪系列的信号重构算法，在正则化正交匹配追踪算法的基础上提出了一种渐进式的正则化自适应匹配追踪算法，仿真研究表明，用所提算法重建的图像在峰值信噪比、相对误差、匹配度及运行时间相比于现有相关算法都有提高。深入研究了分块压缩传感理论，在图像自适应分块压缩传感重建模型的基础之上，根据水声图像的特点，提出了一种新的图像自适应分块压缩传感重建算法，所提算法消除了图像中目标区域相邻块之间的块效应，提高了图像的重建质量，方便后续的目标特征提取及识别。
The thesis discussed the acoustic lens imaging technology. Four main contents wereincluded in the thesis: improving the model of sound field generated by acoustic lens;analyzing and designing acoustic lens system; testing in a tank; compressive sening theorythan can be applied in acoustic lens imaging technology.
The hybrid geometric/wave acoustic model was introduced in detail, and that theadvantages and disadvantages were analyzed. The improved hybrid geometric/wave acousticmodels were proposed. Detailed structure and implementation steps of improved model weregiven. Experimental results were compared to simulation. Therefrom, the improved modelhad more accuracy than the original hybrid model.
The performance of an acoustic lens system was analyzed using the improved hybridmodel, such as focal region and parameter’s influnce on beampatterns of acoustic lens. Theparameters of lens system were geometry、material、tempreture、frequency and so on. Basedon the simulation results, four acoustic lens systems were designed: single-element acousticlens system; two-element acoustic lens system; two-demension acoustic lens; compoundacoustic lens system. The simulation and picture of the designed acoustic lens system weregiven.
The tank tests were operated for the designed acoustic lens system. The test were dividedinto two parts. One was the beamforming test of the acoustic lens system, the other wasbeamforming and imaging test of the prototype. The former concluded testing the focalposition, drawing the beampatterns, measuring the field of view and practical range. Thecomparison between the simulation and test results verified the good accuracy of theimproved hybrid model in modeling the sound field. The latter concluded testing thebeamwidth of the prototype in the horizontal axis, testing the fied-of-view in the horizontaland vertical axis, testing the pratical range and range resolution and imaging the objects withshape of circle and cross. The images formed by the prototype had clearcutness and rapidupdate rate. Therefor, acoustic lens system were available in underwater acoustic imaging.
Compressed sensing theory had potential application in acoustic lens image technology.Three aspects of compressed sensing theory: sparse representation, acquring measured dataand reconstruction of signal were introduced in detail.Orthogonal matching pursuit series theory was researched implemented. A gradual regularized adaptive matching pursuit was presented. Simulation results proved that theproposed algorithm improved peak signal noise ratio、relative error、matching and runtimeof reconstructed image. Block compressed sensing theory was the other research focus.According to the characteristics of underwater acoustic image, a new adaptive blockcompressed sensing reconstructing scheme was proposed. The improving algorithmeliminated blocking effect between neighboring subblocks in target area. The quality ofreconstructed image was increased which was convenient for feature extraction and targetidentification.
引文
[1]沈游.现代技术条件下的海战体系.现代船舰[J].1996:1页
[2]林仁华，金涛等.走向21世纪的海战[M].北京：科学普及出版社，1995
[3]P. S. Green, J.L.S.Bellin,G.C.Knollman. Acoustic Imaging in a Turbid YnderwaterEnvironment. The Journal of the Acoustical Society of Ameica.1968,44(6):1719-1730P
[4] D. L. Folds and D. H. Brown. Focusing properties of cylindrical linquid-filled acousticlenses with large diameter-to-wavelength ratios. The Journal of the Acoustical Society ofAmerica.1968,43(3)
[5]Henry R. Feldman. Nearly Spherical Acoustic Lenses. The Journal of the AcousticalSociety of America.1969,45(4):868-871P
[6]J.L.DuBois, W.Rosenblum, R.Gilmore. Compound Acoustic Lens System. The Journal ofthe Acoustical Society of America.1970,48(2):430-431P
[7]G.C.Knoliman, A.E.Brown, J.L.Weaver,J.L.S.Bellin. Experimental Hydroacoustic ImagingSystem. Journal of Applied Physics.1971,42(6):2168-2180P
[8] D. L. Folds. Focusing properties of a cylindrical linquid-filled compound acoustic lens.The Journal of the Acoustical Society of America.1971,49(5)
[9]G.C.Knoliman, J.L.S.Bellin,J.L.WEAVER. Variable-Focus Liquid-Filled HydroacousticLens. The Journal of the Acoustical Society of America.1971,49(1):253-261P
[10] D. L. Folds. Focusing properties of solid ultrasonic cylindrical lenses.The Journal of theAcoustical Society of America.1973,53(3):826-834P
[11]D. L. Folds and J. Hanlin. Focusing properties of a solid four-element ultrasonic lens.TheJournal of the Acoustical Society of America.1975,58(1):72-77P
[12]Kevin T. Corbett, F.H.Middleton,R.L.Sternberg. Nonspherical acoustic lens study. TheJournal of the Acoustical Society of America.1976,59(5):1104-1109P
[13]Larry Schlussler. The design and test results for an acoustic lens with elliptic surfaces.The journal of the Acoustical Society of America.1980,67(2):699-701P
[14]Edward O.Belcher. Multibeam,diver-held sonar using a liquid-filled,spherical acousticlens. Proceedings of the Conference on Oceans’93.Part3.Victoria,BC,Can.1993:398-402P
[15]Edward O.Belcher. Application of Thin, Acoustic Lenses in a32-Beam,Dual-Frequency,diver-held sonar. Proceedings of1996MTS/IEEE Oceans Conference. Part Suppl. FortLauderdale,FL,USA.IEEE,Piscataway,NJ,United States,1996:767-772P
[16]Belcher. E.O. Thin acoustic lenses applied in64-beam,750kHz diver-held sonar.Ocean’97.MTS/IEEE Conference Proceedings.1997,1:451-456P
[17]Edward O. Belcher, Dana C. Lynn, Hien Q. Dinh etal. LaughlinM, Beamforming andimaging with acoustic lenses in small, high-frequency sonars. The Proceedings ofOceans’99Conference[C].1999:1495-1499P
[18]Thomas E. Linnenbrink, Charles S. Desilets, Marshall K etal. Acoustic lens-basedswimmer’s sonar[A]. Proceedings of SPIE, the international Society for OpticalEngineering[C].1999,3711:43-56P
[19]Edward O.Belcher and Hien Q.Dinh. Limpet Mine Imaging Sonar(LIMIS). Proceeding ofSPIE.1999,3711
[20]Edward Belcher, Brian Matsuyama and Gary Trimble. Object identification with acousticlenses. Proceedings of Oceans2001Conference[C].2001,1:6-11P
[21]桑恩方，庞永杰，卞红雨.水下机器人技术[J].机器人技术与应用.2003,3:8-13页
[22]Y. Takase, T. Anada, T.Tsuchiya etal. Real-time sonar system using acoustic lens andnumerical analysis based on2D/3D parabolic equation method[A]. Oceans-Europe[C].2005:23-28P
[23]Houston.K.M. Three-dimensinal acoustical imaging using micromechanical hydrophones.OCEANS’95.MTS/IEEE.’Challenges of Our Changing Global Environment’.ConferenceProceedings.1995,2:1174-1182P
[24]卞红雨，桑恩方，纪祥春等.声透镜波束形成技术仿真研究[J].哈尔滨工程大学学报.2004,25(1):43-45页
[25]高健波，张福成，赵恒元.菲涅耳声透镜聚焦性能的研究[J].声学学报.1988,13(5):369-375页
[26]傅临泰.圆柱液体声透镜[J].舰船科学技术.1992:7-11页
[27]耿成德等.声纳用圆柱声透镜试验研究[J].声学与电子工程.1993:39-44页
[28]卞红雨，桑恩方.可用于高分辨率成像声纳的声透镜波束形成技术[J].声学与电子工程.2002增刊：28-30页
[29]李颂文.声透镜波束形成技术[J].声学技术.2007,26(5):771-774页
[30]余雅松，王锦柏.柱面型声透镜设计及验证[J].声学技术.2008,27(2):150-155页
[31]李颂文.声透镜成像声纳的高速成像收发通道设计[J].哈尔滨工程大学学报.2010,32(3):286-292页
[32]王勇,李全禄等.柱面声透镜声场分析[J].压电与声光.2011,33(5):695-701页
[33]卞红雨，吴菊.声透镜波束形成和目标成像仿真与试验[J].哈尔滨工程大学学报.2010,31(7):819-824页
[34]Ju Wu,Hongyu Bian. Beam-forming and imaging using acoustic lenses:Some Simulationand Experimental Results.20102ndInternational Conference on Signal ProcessingSystem(ICSPS).2010,2:764-768P
[35]ZEMAX Development corporation. Optical Design Program User’s Guide[R].Bellevue,WA,2006
[36]W.L.Beaver, D.H.Dameron, and A.Macivski. Ultrasonic Imaging with an Acoustic Lens.IEEE Transaction on Sonics and Ultrasonics.1977,24(4)
[37]J.T.Oh and S.B.Park. Optimal Design of a Transmit/Receive System with Reflector and aTransducer Lens for Long-Range Underwater Acoustic Imaging. IEEE1991UltrasonicsSymposium Proceedings.1991,2:949-952P
[38]J.T.Oh and S.B.Park. Iterative design of a lensed transducer-reflector system forunderwater acoustic imaging. The Journal of the Acoustical Society of America.1993,93(2):1166-1174P
[39]N.N.Makarchenko, F.V.Rozhin and O.S.Tonakanov. On Choosing the Optimal Parametersof a Spherical Lens. Vestnik Moskovskogo Universiteta, Seriya3.1989,44(5):32-36P
[40]A.Penttinen and M.Luukkala. Sound pressure near the focal area of an ultrasonic lens.Journal of Physics D:Aplied Physics.1976,9:1927-1936P
[41]Tetsuo ANADA, Takenobu,TSUCHIYA,NobuyukiENDOH. Numerrical Analusis ofUnderwater Acoustic Lens Using Wide-Angle Parabolic Equation Method.Jpn.J.Appl.Phys.2002,41(5):3509-3512P
[42]Toshiaki NAKAMURA, Yuji SATO, Tomoo KAMAKURA. Sound Pressure FieldsFocused Using Biconcave Acoustic Lens for Normal Incidence.Jpn.J.Appl.Phys.2004,43(5):3163-3168P
[43]Kazuyoshi Mori, Toshiaki Nakamura, Tomki Yokoyama.3-D FDTD Analysis of SoundField Focused by Biconcave Acoustic Lens for Normal Incidence.Jpn.J.Appl.Phys.2005,44(6):4696-4701P
[44]Yuji Sato, Ayano Miyazaki, Kazuyoshi Mori. Design of an Absolutely Aplanatic AcousticLens. Jpn.J.Appl.Phys.2007,44(6):4982-4989P
[45]E.J.Candes,J.Romberd and T.Tao.Robust Uncertainty Principles:Exact SignalReconstruction from Highly Incomplete Frequency Information.2004
[46]E Candès. Compressive sampling[A].Proceedings of the International Congress ofMathematician[C]s.Madrid,Spain,2006,3:1433-1452P
[47]D L Donoho. Compressed sensing[J].IEEE Trans. On Information Theory.2006,52(4):1289-1306P
[48]E Candès and J Romberg. Quantitative robust uncertainty principles and optimally sparsedecompositions[J].Foundations of Comput Math,2006,6(2):227-254P
[49]D L Donoho,Y Tsaig. Extensions of compressed sensing[J].Signal Processing.2006,86(3):533-548P
[50]E J Candès, J Romberg. Pratical signal recovery from random projections[OL].http://www.acm.caltech.edu/~emmanual/papers/Pratical Recovery.pdf
[51]Gilbert AC, Guha S, Indyk P, etal. Near-optimal sparse Fourier representations viasampling[C].2002ACM Symposium on Theory of Computing. Montreal,Quebec,Canada,2002:152-161P
[52]Takhar D, Laska J,Wakin M, etal. A new compressive imaging camera architecture usingoptical-domain compression[C].SPIE Electronic Imaging. San Jose, California,2006
[53]Duarte M F,Davenport M A,Takhar D,Sun T,Kelly K F,Baraniuk R G. Single-pixelimaging via compressive sampling[J].IEEE Signal Processing Magazine,2008,25(2):83-91P
[54]刘伯胜,雷家煜.水声学原理[M].哈尔滨:哈尔滨工程大学出版社,2010:101-118页
[55] FINK K. Computer simulation of pressure fields generated by acoustic lensbeamformers[D]. University of Washington,1994
[56]马科斯.玻恩,埃米尔.沃耳夫.杨葭荪译.光学原理[M].第七版.北京:电子工业出版社,2009:342-352页
[57]Terence A. Cornelius and Kevin L. Williams. Note on the Calculation of the SphericallyAberrated Field of an Acoustic Lens. Technical Memorandum APL-UW TM7-92,Applied Physics Laboratory, University of Washington,1992
[58]Personal Communicaton.1993
[59]D.L.Folds. Status of Ultrasonic Lens Development. Underwater Acoustics and SignalProcessing. D. Reidel Publishing Company.1981:263-279P
[60]刘东宇.声透镜波束形成和水声图象处理技术研究[D].哈尔滨工程大学硕士学位论文.2009
[61]王荣津等.水声材料手册[M].北京:科学出版社,1983
[62]牛凤岐，张雪萍.国产塑料的高频声学特性[J].应用声学.1987,7(2):14-17页
[63] Candes E, Romberg J,Tao T. Robust Uncertainty Principles:Exact Signal Reconstructionfrom Highly Incomplete Frequency Information.IEEE Transactions on InformationTheory,2006,52(2):489-509P
[64]Baraniuk R G. Compressive sensing. IEEE Signal Processing Magazine.2007,24(4):118-121P
[65]Compressive Sensing Resources[OL].http://dsp.rice.edu/cs
[66]傅迎华.可压缩传感重构算法与近似QR分解[J].计算机应用.2008,28(9):2300-2302页
[67]方红，章权兵，韦穗.基于亚高斯随机投影的图像重建方法[J].计算机研究与发展，2008,45(8):1402-1407页
[68]练秋生，高彦彦，陈书贞.基于两步迭代收缩法和复数小波的压缩传感图像重构[J].仪器仪表学报.2009,30(7):1426-1431页
[69]肖龙帅，黄华，夏建刚，李灵.压缩传感方位估计[J].通信技术.2009,42(11):182-184页
[70]余惠敏，方广有.压缩感知理论在探地雷达三维成像中的应用[J].电子与信息学报.2010,32(1):12-16页
[71]Gehm M E,John R,Brady D J,Willet R M,Schulz T J. Single-shot compressive spectralimaging with a dual-disperse architecture.Optics Express,2007,15(21):14013-14027P
[72]Gehm M E,John R,Brady D J,Willet R M,Schulz T J. Single-shot compressive spectralimaging with a dual-disperse architecture.Optics Express,2007,15(21):14013-14027P
[73]Takhar D, Bansal V, Wakin M, etal. A compressed sensing camera: New theory and animplementation using digital micromirrors[C].SPIE Electronic Imaging: ComputationalImaging. San Jose, California,2006
[74]Wakin MB, Laska J. Compressive imaging for video representation andcoding[C].Picture Coding Symposium(PCS):Bei jing, China,2006
[75] Haupt J, Nowak R. Compressive sampling for signal detection. In:Proceedings of theIEEE Inernational Conference on Acoustics, Speech and Signal Proceedings. WashingtonD.C.,USA:2007:1509-1512P
[76]Davenport M A, Wakin M B,Baraniuk R G. Detection and Estimation with CompressiveMeasurements, Technical Report TREE0610, Department of Electrical Engineering,Rice University, USA,2006
[77]Haupt J,Castro R,Nowak R,Fudge G,Yeh A. Compessive sampling for signalclassification. In:Proceedings of the Fortieth Asilomar Conference on theSignals,Systems and Computers. Washington D.C.,USA:IEEE,2006:1430-1434P
[78]Taubock G,Hlawatsch F. A compressed sensing technique for OFDM channel estimationin mobile environments:exploiting channel sparsity for reducing pilots.In:Proceedings ofthe IEEE International Conference on Acoustics, Speech,and Signal Processing.LasVegas,USA:IEEE,2008:1885-2888P
[79]Bajwa W U,Haupt J,Raz G,Nowak R. Compressed channel sensing.In:Proceedings of the42nd Annual Conference on Information Science and Systems.WashingtonD.C.,USA:IEEE,2008:5-10P
[80]Bajwa W,Haupt J,Sayeed A,Nowak R. Compressive wireless sensing.In:Proceedings ofthe International Conference on Information Processing in Sensor Netwoks.Nashville,USA:ACM,2006:134-142P
[81]Hennenfent G,Herrmann F J. Simply denoise:wave field reconstruction via jitteredundersampling.Geophysics,2008,73(3):19-28P
[82]Olshausen B A, Field D J. Emergence of simple-cell receptive field properties by learninga sparse code for narural images. Nature,1996,381(6583):607-609P
[83]Mallat S. A Wavelet Tour of Signal Processing. San Diego: Academic Press,1996
[84]Cabdes E. and Romberg J. Sparsity and incoherence in compressive sampling. InverseProblems,2007,23(3):969-985P.
[85]孙玉宝，肖亮，韦志辉，邵文泽.基于Gabor感知多成份字典的图像稀疏表示算法研究[J].自动化学报.2008,34(11):1379-1387页
[86]Rauhut H,Schnass K,Vandergheynst P. Compressed sensing and redundantdictionaries.IEEE Transactions on Information Theory.2008,54(5):2210-2219P
[87]Candes E, Tao T. Decoding by linear programming. IEEE Transactions on InformationTheory.2005,51(12):4203-4215P
[88]Candes E, Romberg J, Tao T. Stable signal recovery from incomplete and inaccuratemeasurements. Communications on Pure and Applied Mathematics,2006,59(8):1207-1223P
[89]R. Baraniuk. A lecture on compressive sensing[J].IEEE Signal Processing Magazine,2007,24(4):118-121P
[90]E. Candes and T. Tao. Near optimal signal recovery from random projections:universalencoding strategies. IEEE Trans. Info. Theory.2006,52(12):5406-5425P
[91]Candes E, Tao T. Near optimal signal recovery from random projections: Universalencoding stratedies? IEEE Transactions on Information Theory.2006,52(12):5406-5425P
[92]Do T T, Trany T D, Gan L. Fast compressive sampling with structurally random matrices.In:Proceedings of the IEEE International Conference on Acoustics, Speech and SignalProcessing. Washington D. C., USA:IEEE,2008:3369-3372P
[93]Donoho D L. For most large underdetermined systems of linear equations, the minimall1norm solution is also the sparsest solution. Communication on Pure and AppliedMathematics.2006,59(6):797-829P
[94]Tropp J A, Gilbert A C. Signal recovery from random measurements via orthogobalmatching pursuit. IEEE Transactions on Information Theory.2007,53(12):4655-4666P
[95]Needell D, Vershynin R. Greedy signal recovery and uncertainty principles. In:Proceedings of the Conference on Computational Imaging. San Jose, USA:SPIE,2008:1-12P
[96]Needell D, Tropp J A. CoSaMP: Iterative signal recovery from incomplete and inaccuratesamples. Applied and Computational Harmonic Analysis.2008,26(3):301-321P
[97]Kingsbury N. Complex wavelets for shift invariant analysis and filtering of complexwavelets for shift invariant analysis and filtering of signals. Applied and ComputationalHarmonic Analysis.2001,10(3):234-253P
[98]Herrity K K, Gilbert A C, Tropp J A. Sparse approximation via iterative thresholding. In:Proceedings og the IEEE International Conference on Acoustics, Speech and SignalProcessing. Washington D. C., USA:IEEE,2006.624-627P
[99]Blumensath T, Davies M E. Iterative thresholding for sparse approximations. Journal ofFourier Analysis and Applications.2008,14(5-6):629-654P
[100]A C Gilbert, S Muthukrishnan, M J Strauss. Improved time bounds for near-optimalsparse Fourier representation[A]. Proceedings of SPIE, Wavelets XI[C]. BellinghamWA:International Society for Optical Engineeing.2005,5914:1-15P
[101]A C Gilbert, M J Strauss, J A Tropp,etc. Algorithmic linear dimension reduction in thenorm for sparse vectors[OL]. http://www. math. ucdavis. edu/~vershynin/papers/algorithmic-dim-reduction.pdf
[102]A C Gilbert, M J Strauss, J S Tropp, etal. One sketch for all: Fast algorithms forcompressed sensing[A]. Proceedings og the39thAnnual ACM Symposium on Theory ofComputing[C]. New York:Association for Computing Mchiner.2007:237-246P
[103]Chen S B, Donoho D L, Saunders M A. Atomic decomposition by basis pursuit. SIAMJournal on Scientific Computing.1998,20(1):33-61P
[104]刘亚新，赵瑞珍，胡绍海，姜春晖.用于压缩感知信号重建的正则化自适应匹配追踪算法[J].电子与信息学报.2010,32(11):2713-2717页
[105]Candes E. and Tao T. Decoding by linear programming. IEEE Transactions onInformation Theory,2005,51(12):4203-4215P
[106]Gan L. Block compressed sensing of natural images[C].Proc Int Conf on Digigal SignalProcessing(DSP),Cardiff,UK,2007
[107]Candes E. J. Compressive sampling.Proceedings of the International Congress ofMathematicians[C]. Madrid,Spain:[s.n.],2006:1433-1452P
[108]She Q S, Luo Z Z, Zhu Y P, etal. Spatially adaptive image reconstruction viacompressive sensing[C].Proceedings of the7thAsian Control Conference. Hong Kong,China,2009:1570-1575P
[109]李蕴华.一种改进的图像分块压缩感知模型[J].计算机工程与应用.2011,47(25):186-193页.
[2]林仁华，金涛等.走向21世纪的海战[M].北京：科学普及出版社，1995
[3]P. S. Green, J.L.S.Bellin,G.C.Knollman. Acoustic Imaging in a Turbid YnderwaterEnvironment. The Journal of the Acoustical Society of Ameica.1968,44(6):1719-1730P
[4] D. L. Folds and D. H. Brown. Focusing properties of cylindrical linquid-filled acousticlenses with large diameter-to-wavelength ratios. The Journal of the Acoustical Society ofAmerica.1968,43(3)
[5]Henry R. Feldman. Nearly Spherical Acoustic Lenses. The Journal of the AcousticalSociety of America.1969,45(4):868-871P
[6]J.L.DuBois, W.Rosenblum, R.Gilmore. Compound Acoustic Lens System. The Journal ofthe Acoustical Society of America.1970,48(2):430-431P
[7]G.C.Knoliman, A.E.Brown, J.L.Weaver,J.L.S.Bellin. Experimental Hydroacoustic ImagingSystem. Journal of Applied Physics.1971,42(6):2168-2180P
[8] D. L. Folds. Focusing properties of a cylindrical linquid-filled compound acoustic lens.The Journal of the Acoustical Society of America.1971,49(5)
[9]G.C.Knoliman, J.L.S.Bellin,J.L.WEAVER. Variable-Focus Liquid-Filled HydroacousticLens. The Journal of the Acoustical Society of America.1971,49(1):253-261P
[10] D. L. Folds. Focusing properties of solid ultrasonic cylindrical lenses.The Journal of theAcoustical Society of America.1973,53(3):826-834P
[11]D. L. Folds and J. Hanlin. Focusing properties of a solid four-element ultrasonic lens.TheJournal of the Acoustical Society of America.1975,58(1):72-77P
[12]Kevin T. Corbett, F.H.Middleton,R.L.Sternberg. Nonspherical acoustic lens study. TheJournal of the Acoustical Society of America.1976,59(5):1104-1109P
[13]Larry Schlussler. The design and test results for an acoustic lens with elliptic surfaces.The journal of the Acoustical Society of America.1980,67(2):699-701P
[14]Edward O.Belcher. Multibeam,diver-held sonar using a liquid-filled,spherical acousticlens. Proceedings of the Conference on Oceans’93.Part3.Victoria,BC,Can.1993:398-402P
[15]Edward O.Belcher. Application of Thin, Acoustic Lenses in a32-Beam,Dual-Frequency,diver-held sonar. Proceedings of1996MTS/IEEE Oceans Conference. Part Suppl. FortLauderdale,FL,USA.IEEE,Piscataway,NJ,United States,1996:767-772P
[16]Belcher. E.O. Thin acoustic lenses applied in64-beam,750kHz diver-held sonar.Ocean’97.MTS/IEEE Conference Proceedings.1997,1:451-456P
[17]Edward O. Belcher, Dana C. Lynn, Hien Q. Dinh etal. LaughlinM, Beamforming andimaging with acoustic lenses in small, high-frequency sonars. The Proceedings ofOceans’99Conference[C].1999:1495-1499P
[18]Thomas E. Linnenbrink, Charles S. Desilets, Marshall K etal. Acoustic lens-basedswimmer’s sonar[A]. Proceedings of SPIE, the international Society for OpticalEngineering[C].1999,3711:43-56P
[19]Edward O.Belcher and Hien Q.Dinh. Limpet Mine Imaging Sonar(LIMIS). Proceeding ofSPIE.1999,3711
[20]Edward Belcher, Brian Matsuyama and Gary Trimble. Object identification with acousticlenses. Proceedings of Oceans2001Conference[C].2001,1:6-11P
[21]桑恩方，庞永杰，卞红雨.水下机器人技术[J].机器人技术与应用.2003,3:8-13页
[22]Y. Takase, T. Anada, T.Tsuchiya etal. Real-time sonar system using acoustic lens andnumerical analysis based on2D/3D parabolic equation method[A]. Oceans-Europe[C].2005:23-28P
[23]Houston.K.M. Three-dimensinal acoustical imaging using micromechanical hydrophones.OCEANS’95.MTS/IEEE.’Challenges of Our Changing Global Environment’.ConferenceProceedings.1995,2:1174-1182P
[24]卞红雨，桑恩方，纪祥春等.声透镜波束形成技术仿真研究[J].哈尔滨工程大学学报.2004,25(1):43-45页
[25]高健波，张福成，赵恒元.菲涅耳声透镜聚焦性能的研究[J].声学学报.1988,13(5):369-375页
[26]傅临泰.圆柱液体声透镜[J].舰船科学技术.1992:7-11页
[27]耿成德等.声纳用圆柱声透镜试验研究[J].声学与电子工程.1993:39-44页
[28]卞红雨，桑恩方.可用于高分辨率成像声纳的声透镜波束形成技术[J].声学与电子工程.2002增刊：28-30页
[29]李颂文.声透镜波束形成技术[J].声学技术.2007,26(5):771-774页
[30]余雅松，王锦柏.柱面型声透镜设计及验证[J].声学技术.2008,27(2):150-155页
[31]李颂文.声透镜成像声纳的高速成像收发通道设计[J].哈尔滨工程大学学报.2010,32(3):286-292页
[32]王勇,李全禄等.柱面声透镜声场分析[J].压电与声光.2011,33(5):695-701页
[33]卞红雨，吴菊.声透镜波束形成和目标成像仿真与试验[J].哈尔滨工程大学学报.2010,31(7):819-824页
[34]Ju Wu,Hongyu Bian. Beam-forming and imaging using acoustic lenses:Some Simulationand Experimental Results.20102ndInternational Conference on Signal ProcessingSystem(ICSPS).2010,2:764-768P
[35]ZEMAX Development corporation. Optical Design Program User’s Guide[R].Bellevue,WA,2006
[36]W.L.Beaver, D.H.Dameron, and A.Macivski. Ultrasonic Imaging with an Acoustic Lens.IEEE Transaction on Sonics and Ultrasonics.1977,24(4)
[37]J.T.Oh and S.B.Park. Optimal Design of a Transmit/Receive System with Reflector and aTransducer Lens for Long-Range Underwater Acoustic Imaging. IEEE1991UltrasonicsSymposium Proceedings.1991,2:949-952P
[38]J.T.Oh and S.B.Park. Iterative design of a lensed transducer-reflector system forunderwater acoustic imaging. The Journal of the Acoustical Society of America.1993,93(2):1166-1174P
[39]N.N.Makarchenko, F.V.Rozhin and O.S.Tonakanov. On Choosing the Optimal Parametersof a Spherical Lens. Vestnik Moskovskogo Universiteta, Seriya3.1989,44(5):32-36P
[40]A.Penttinen and M.Luukkala. Sound pressure near the focal area of an ultrasonic lens.Journal of Physics D:Aplied Physics.1976,9:1927-1936P
[41]Tetsuo ANADA, Takenobu,TSUCHIYA,NobuyukiENDOH. Numerrical Analusis ofUnderwater Acoustic Lens Using Wide-Angle Parabolic Equation Method.Jpn.J.Appl.Phys.2002,41(5):3509-3512P
[42]Toshiaki NAKAMURA, Yuji SATO, Tomoo KAMAKURA. Sound Pressure FieldsFocused Using Biconcave Acoustic Lens for Normal Incidence.Jpn.J.Appl.Phys.2004,43(5):3163-3168P
[43]Kazuyoshi Mori, Toshiaki Nakamura, Tomki Yokoyama.3-D FDTD Analysis of SoundField Focused by Biconcave Acoustic Lens for Normal Incidence.Jpn.J.Appl.Phys.2005,44(6):4696-4701P
[44]Yuji Sato, Ayano Miyazaki, Kazuyoshi Mori. Design of an Absolutely Aplanatic AcousticLens. Jpn.J.Appl.Phys.2007,44(6):4982-4989P
[45]E.J.Candes,J.Romberd and T.Tao.Robust Uncertainty Principles:Exact SignalReconstruction from Highly Incomplete Frequency Information.2004
[46]E Candès. Compressive sampling[A].Proceedings of the International Congress ofMathematician[C]s.Madrid,Spain,2006,3:1433-1452P
[47]D L Donoho. Compressed sensing[J].IEEE Trans. On Information Theory.2006,52(4):1289-1306P
[48]E Candès and J Romberg. Quantitative robust uncertainty principles and optimally sparsedecompositions[J].Foundations of Comput Math,2006,6(2):227-254P
[49]D L Donoho,Y Tsaig. Extensions of compressed sensing[J].Signal Processing.2006,86(3):533-548P
[50]E J Candès, J Romberg. Pratical signal recovery from random projections[OL].http://www.acm.caltech.edu/~emmanual/papers/Pratical Recovery.pdf
[51]Gilbert AC, Guha S, Indyk P, etal. Near-optimal sparse Fourier representations viasampling[C].2002ACM Symposium on Theory of Computing. Montreal,Quebec,Canada,2002:152-161P
[52]Takhar D, Laska J,Wakin M, etal. A new compressive imaging camera architecture usingoptical-domain compression[C].SPIE Electronic Imaging. San Jose, California,2006
[53]Duarte M F,Davenport M A,Takhar D,Sun T,Kelly K F,Baraniuk R G. Single-pixelimaging via compressive sampling[J].IEEE Signal Processing Magazine,2008,25(2):83-91P
[54]刘伯胜,雷家煜.水声学原理[M].哈尔滨:哈尔滨工程大学出版社,2010:101-118页
[55] FINK K. Computer simulation of pressure fields generated by acoustic lensbeamformers[D]. University of Washington,1994
[56]马科斯.玻恩,埃米尔.沃耳夫.杨葭荪译.光学原理[M].第七版.北京:电子工业出版社,2009:342-352页
[57]Terence A. Cornelius and Kevin L. Williams. Note on the Calculation of the SphericallyAberrated Field of an Acoustic Lens. Technical Memorandum APL-UW TM7-92,Applied Physics Laboratory, University of Washington,1992
[58]Personal Communicaton.1993
[59]D.L.Folds. Status of Ultrasonic Lens Development. Underwater Acoustics and SignalProcessing. D. Reidel Publishing Company.1981:263-279P
[60]刘东宇.声透镜波束形成和水声图象处理技术研究[D].哈尔滨工程大学硕士学位论文.2009
[61]王荣津等.水声材料手册[M].北京:科学出版社,1983
[62]牛凤岐，张雪萍.国产塑料的高频声学特性[J].应用声学.1987,7(2):14-17页
[63] Candes E, Romberg J,Tao T. Robust Uncertainty Principles:Exact Signal Reconstructionfrom Highly Incomplete Frequency Information.IEEE Transactions on InformationTheory,2006,52(2):489-509P
[64]Baraniuk R G. Compressive sensing. IEEE Signal Processing Magazine.2007,24(4):118-121P
[65]Compressive Sensing Resources[OL].http://dsp.rice.edu/cs
[66]傅迎华.可压缩传感重构算法与近似QR分解[J].计算机应用.2008,28(9):2300-2302页
[67]方红，章权兵，韦穗.基于亚高斯随机投影的图像重建方法[J].计算机研究与发展，2008,45(8):1402-1407页
[68]练秋生，高彦彦，陈书贞.基于两步迭代收缩法和复数小波的压缩传感图像重构[J].仪器仪表学报.2009,30(7):1426-1431页
[69]肖龙帅，黄华，夏建刚，李灵.压缩传感方位估计[J].通信技术.2009,42(11):182-184页
[70]余惠敏，方广有.压缩感知理论在探地雷达三维成像中的应用[J].电子与信息学报.2010,32(1):12-16页
[71]Gehm M E,John R,Brady D J,Willet R M,Schulz T J. Single-shot compressive spectralimaging with a dual-disperse architecture.Optics Express,2007,15(21):14013-14027P
[72]Gehm M E,John R,Brady D J,Willet R M,Schulz T J. Single-shot compressive spectralimaging with a dual-disperse architecture.Optics Express,2007,15(21):14013-14027P
[73]Takhar D, Bansal V, Wakin M, etal. A compressed sensing camera: New theory and animplementation using digital micromirrors[C].SPIE Electronic Imaging: ComputationalImaging. San Jose, California,2006
[74]Wakin MB, Laska J. Compressive imaging for video representation andcoding[C].Picture Coding Symposium(PCS):Bei jing, China,2006
[75] Haupt J, Nowak R. Compressive sampling for signal detection. In:Proceedings of theIEEE Inernational Conference on Acoustics, Speech and Signal Proceedings. WashingtonD.C.,USA:2007:1509-1512P
[76]Davenport M A, Wakin M B,Baraniuk R G. Detection and Estimation with CompressiveMeasurements, Technical Report TREE0610, Department of Electrical Engineering,Rice University, USA,2006
[77]Haupt J,Castro R,Nowak R,Fudge G,Yeh A. Compessive sampling for signalclassification. In:Proceedings of the Fortieth Asilomar Conference on theSignals,Systems and Computers. Washington D.C.,USA:IEEE,2006:1430-1434P
[78]Taubock G,Hlawatsch F. A compressed sensing technique for OFDM channel estimationin mobile environments:exploiting channel sparsity for reducing pilots.In:Proceedings ofthe IEEE International Conference on Acoustics, Speech,and Signal Processing.LasVegas,USA:IEEE,2008:1885-2888P
[79]Bajwa W U,Haupt J,Raz G,Nowak R. Compressed channel sensing.In:Proceedings of the42nd Annual Conference on Information Science and Systems.WashingtonD.C.,USA:IEEE,2008:5-10P
[80]Bajwa W,Haupt J,Sayeed A,Nowak R. Compressive wireless sensing.In:Proceedings ofthe International Conference on Information Processing in Sensor Netwoks.Nashville,USA:ACM,2006:134-142P
[81]Hennenfent G,Herrmann F J. Simply denoise:wave field reconstruction via jitteredundersampling.Geophysics,2008,73(3):19-28P
[82]Olshausen B A, Field D J. Emergence of simple-cell receptive field properties by learninga sparse code for narural images. Nature,1996,381(6583):607-609P
[83]Mallat S. A Wavelet Tour of Signal Processing. San Diego: Academic Press,1996
[84]Cabdes E. and Romberg J. Sparsity and incoherence in compressive sampling. InverseProblems,2007,23(3):969-985P.
[85]孙玉宝，肖亮，韦志辉，邵文泽.基于Gabor感知多成份字典的图像稀疏表示算法研究[J].自动化学报.2008,34(11):1379-1387页
[86]Rauhut H,Schnass K,Vandergheynst P. Compressed sensing and redundantdictionaries.IEEE Transactions on Information Theory.2008,54(5):2210-2219P
[87]Candes E, Tao T. Decoding by linear programming. IEEE Transactions on InformationTheory.2005,51(12):4203-4215P
[88]Candes E, Romberg J, Tao T. Stable signal recovery from incomplete and inaccuratemeasurements. Communications on Pure and Applied Mathematics,2006,59(8):1207-1223P
[89]R. Baraniuk. A lecture on compressive sensing[J].IEEE Signal Processing Magazine,2007,24(4):118-121P
[90]E. Candes and T. Tao. Near optimal signal recovery from random projections:universalencoding strategies. IEEE Trans. Info. Theory.2006,52(12):5406-5425P
[91]Candes E, Tao T. Near optimal signal recovery from random projections: Universalencoding stratedies? IEEE Transactions on Information Theory.2006,52(12):5406-5425P
[92]Do T T, Trany T D, Gan L. Fast compressive sampling with structurally random matrices.In:Proceedings of the IEEE International Conference on Acoustics, Speech and SignalProcessing. Washington D. C., USA:IEEE,2008:3369-3372P
[93]Donoho D L. For most large underdetermined systems of linear equations, the minimall1norm solution is also the sparsest solution. Communication on Pure and AppliedMathematics.2006,59(6):797-829P
[94]Tropp J A, Gilbert A C. Signal recovery from random measurements via orthogobalmatching pursuit. IEEE Transactions on Information Theory.2007,53(12):4655-4666P
[95]Needell D, Vershynin R. Greedy signal recovery and uncertainty principles. In:Proceedings of the Conference on Computational Imaging. San Jose, USA:SPIE,2008:1-12P
[96]Needell D, Tropp J A. CoSaMP: Iterative signal recovery from incomplete and inaccuratesamples. Applied and Computational Harmonic Analysis.2008,26(3):301-321P
[97]Kingsbury N. Complex wavelets for shift invariant analysis and filtering of complexwavelets for shift invariant analysis and filtering of signals. Applied and ComputationalHarmonic Analysis.2001,10(3):234-253P
[98]Herrity K K, Gilbert A C, Tropp J A. Sparse approximation via iterative thresholding. In:Proceedings og the IEEE International Conference on Acoustics, Speech and SignalProcessing. Washington D. C., USA:IEEE,2006.624-627P
[99]Blumensath T, Davies M E. Iterative thresholding for sparse approximations. Journal ofFourier Analysis and Applications.2008,14(5-6):629-654P
[100]A C Gilbert, S Muthukrishnan, M J Strauss. Improved time bounds for near-optimalsparse Fourier representation[A]. Proceedings of SPIE, Wavelets XI[C]. BellinghamWA:International Society for Optical Engineeing.2005,5914:1-15P
[101]A C Gilbert, M J Strauss, J A Tropp,etc. Algorithmic linear dimension reduction in thenorm for sparse vectors[OL]. http://www. math. ucdavis. edu/~vershynin/papers/algorithmic-dim-reduction.pdf
[102]A C Gilbert, M J Strauss, J S Tropp, etal. One sketch for all: Fast algorithms forcompressed sensing[A]. Proceedings og the39thAnnual ACM Symposium on Theory ofComputing[C]. New York:Association for Computing Mchiner.2007:237-246P
[103]Chen S B, Donoho D L, Saunders M A. Atomic decomposition by basis pursuit. SIAMJournal on Scientific Computing.1998,20(1):33-61P
[104]刘亚新，赵瑞珍，胡绍海，姜春晖.用于压缩感知信号重建的正则化自适应匹配追踪算法[J].电子与信息学报.2010,32(11):2713-2717页
[105]Candes E. and Tao T. Decoding by linear programming. IEEE Transactions onInformation Theory,2005,51(12):4203-4215P
[106]Gan L. Block compressed sensing of natural images[C].Proc Int Conf on Digigal SignalProcessing(DSP),Cardiff,UK,2007
[107]Candes E. J. Compressive sampling.Proceedings of the International Congress ofMathematicians[C]. Madrid,Spain:[s.n.],2006:1433-1452P
[108]She Q S, Luo Z Z, Zhu Y P, etal. Spatially adaptive image reconstruction viacompressive sensing[C].Proceedings of the7thAsian Control Conference. Hong Kong,China,2009:1570-1575P
[109]李蕴华.一种改进的图像分块压缩感知模型[J].计算机工程与应用.2011,47(25):186-193页.