[1] Lefsky M A, Cohen W B, Parker G G, et al. Lidar remote sensing for ecosystem studies[J]. BioScience, 2002, 52(1): 19-30.

[2] Wu SH, Zhai XC, Liu BY, et al. Characterization of aircraft dynamic wake vortices and atmospheric turbulence by coherent doppler lidar[C]∥The 28th International Laser Radar Conference, June 25-30, 2017, Bucharest, Romania. Amsterdam: EDP Sciences, 2018, 176: 06001.

[3] Wang C, Xia H Y, Shangguan M J, et al. 1.5 μm polarization coherent lidar incorporating time-division multiplexing[J]. Optics Express, 2017, 25(17): 20663-20674.

[4] 麻晓敏, 陶宗明, 张璐璐, 等. 侧向散射激光雷达探测白天近地面气溶胶探测技术[J]. 光学学报, 2018, 38(4): 0401005.

    Ma X M, Tao Z M, Zhang L L, et al. Ground layer aerosol detection technology during daytime based on side-scattering lidar[J]. Acta Optica Sinica, 2018, 38(4): 0401005.

[5] 叶光豪, 邓愫愫, 徐文兵, 等. 机载激光雷达技术应用于沙丘变形监测的研究[J]. 激光与光电子学进展, 2018, 55(5): 052802.

    Ye G H, Deng S S, Xu W B, et al. Application of airborne LiDAR technology in dune deformation monitoring[J]. Laser & Optoelectronics Progress, 2018, 55(5): 052802.

[6] Cohen L. Time-frequency distributions-a review[J]. Proceedings of the IEEE, 1989, 77(7): 941-981.

[7] Feng Z P, Liang M, Chu F L. Recent advances in time-frequency analysis methods for machinery fault diagnosis: a review with application examples[J]. Mechanical Systems and Signal Processing, 2013, 38(1): 165-205.

[8] BoashashB. Time-frequency signal analysis and processing: a comprehensive reference[M]. New York: Academic Press, 2015.

[9] Gabor D. Theory of communication. Part 1: The analysis of information[J]. Journal of the Institution of Electrical Engineers-Part III: Radio and Communication Engineering, 1946, 93(26): 429-441.

[10] Potter RK, Kopp GA, Green HC. Visible speech[M]. New York: Van Nostrand and Company, 1947.

[11] Grossmann A, Morlet J. Decomposition of hardy functions into square integrable wavelets of constant shape[J]. SIAM Journal on Mathematical Analysis, 1984, 15(4): 723-736.

[12] Stockwell R G, Mansinha L, Lowe R P. Localization of the complex spectrum: the S transform[J]. IEEE Transactions on Signal Processing, 1996, 44(4): 998-1001.

[13] Namias V. The fractional order Fourier transform and its application to quantum mechanics[J]. IMA Journal of Applied Mathematics, 1980, 25(3): 241-265.

[14] CohenL. Time-frequency analysis[M]. New Jersey: Prentice Hall PTR, 1995.

[15] BertrandJ, BertrandP. Affinetime-frequency distributions[C]∥1990 Special Conference on Time-Frequency Signal Analysis/International Symp on Signal Processing and its Applications, 1990, Gold Coast Australia. Melbourne: Longman Cheshire, 1992: 118- 140.

[16] Auger F, Flandrin P. Improving the readability of time-frequency and time-scale representations by the reassignment method[J]. IEEE Transactions on Signal Processing, 1995, 43(5): 1068-1089.

[17] Jeong J, Williams W J. Kernel design for reduced interference distributions[J]. IEEE Transactions on Signal Processing, 1992, 40(2): 402-412.

[18] Wigner E. On the quantum correction for thermodynamic equilibrium[J]. Physical Review, 1932, 40(5): 749-759.

[19] Ville J. Theorie et application de la notion de signal analytique[J]. Cables et Transmission, 1948, 2(1): 61-74.

[20] Bastiaans M J. A sampling theorem for the complex spectrogram, and Gabor's expansion of a signal in Gaussian elementary signals[J]. Optical Engineering, 1981, 20(4): 204594.

[21] Claasen T. Mecklenbrauker W F G. The Wigner distribution—a tool for time-frequency signal analysis[J]. Philips Journal of Research, 1980, 35(3): 217-250.

[22] FlandrinP, MartinW. A general class of estimators for the Wigner-Ville spectrum of non-stationary processes[M] ∥Bensoussan A, Lions J L. Analysis and Optimization of Systems. Berlin, Heidelberg: Springer, 1984: 15- 23.

[23] Born M, Jordan P. Zur quantenmechanik[J]. Zeitschriftfür Physik, 1925, 34(1): 858-888.

[24] Choi H I, Williams W J. Improved time-frequency representation of multicomponent signals using exponential kernels[J]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1989, 37(6): 862-871.

[25] Zhao Y, Atlas L E, Marks R J. The use of cone-shaped kernels for generalized time-frequency representations of nonstationary signals[J]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1990, 38(7): 1084-1091.

[26] Page C H. Instantaneous power spectra[J]. Journal of Applied Physics, 1952, 23(1): 103-106.

[27] Rihaczek A. Signal energy distribution in time and frequency[J]. IEEE Transactions on Information Theory, 1968, 14(3): 369-374.

[28] Margenau H, Hill R N. Correlation between measurements in quantum theory[J]. Progress of Theoretical Physics, 1961, 26(5): 722-738.

[29] Huang N E, Shen Z, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings: Mathematical, Physical and Engineering Sciences, 1998, 454(1971): 903-995.

[30] Huang NE. Hilbert-Huang transform and its applications[M]. 2nd ed. New Jersey: World Scientific, 2014.

[31] Qian S E, Chen D P. Signal representation using adaptive normalized Gaussian functions[J]. Signal Processing, 1994, 36(1): 1-11.

[32] Mallat S G, Zhang Z F. Matching pursuits with time-frequency dictionaries[J]. IEEE Transactions on Signal Processing, 1993, 41(12): 3397-3415.

[33] Képesi M, Weruaga L. Adaptive chirp-based time-frequency analysis of speech signals[J]. Speech Communication, 2006, 48(5): 474-492.

[34] BrousmicheS. Simulation of coherent Doppler LIDAR signals and their analysis with the Cohen's class: application to algorithms design for wake vortex detection and characterization[D]. Belgium: UCL-Université Catholique deLouvain, 2010.

[35] RenardW, GoularD, VallaM, et al. Beyond 10 km range wind-speed measurement with a 1.5 μm all-fiber laser source[C]∥2014 Conference on Lasers and Electro-Optics (CLEO)-Laser Science to Photonic Applications, June 8-13, 2014, San Jose, CA, USA. New York: IEEE, 2014: 14822367.

[36] Dolfi-Bouteyre A, Canat G, Lombard L, et al. Long-range wind monitoring in real time with optimized coherent lidar[J]. Optical Engineering, 2017, 56(3): 031217.

[37] Qiu JH, Shen SP, Xu GY. Short-term wind speed forecasting by combination of masking signal-based empirical mode decomposition and extreme learning machine[C]∥2016 11th International Conference on Computer Science & Education (ICCSE), August 23-25, 2016, Nagoya, Japan. New York: IEEE, 2016: 581- 586.

[38] Chen C, Chu X Z. Two-dimensional Morlet wavelet transform and its application to wave recognition methodology of automatically extracting two-dimensional wave packets from lidar observations in Antarctica[J]. Journal of Atmospheric and Solar-Terrestrial Physics, 2017, 162: 28-47.

[39] Chen C, Chu X Z, Zhao J, et al. Lidar observations of persistent gravity waves with periods of 3-10 h in the Antarctic middle and upper atmosphere at McMurdo (77.83°S, 166.67°E)[J]. Journal of Geophysical Research: Space Physics, 2016, 121(2): 1483-1502.

[40] Cézard N, Liméry A, Bertrand J, et al. New lidar challenges for gas hazard management in industrial environments[J]. Proceedings of SPIE, 2017, 10429: 1042903.

[41] Kaifler N, Kaifler B, Ehard B, et al. Observational indications of downward-propagating gravity waves in middle atmosphere lidar data[J]. Journal of Atmospheric and Solar-Terrestrial Physics, 2017, 162: 16-27.

[42] Wang C, Xia H Y, Liu Y P, et al. Spatial resolution enhancement of coherent Doppler wind lidar using joint time-frequency analysis[J]. Optics Communications, 2018, 424: 48-53.

[43] Boyo H, Fujiwara M, Moshnyaga V G, et al. Algorithm based on joint time-frequency analysis to eliminate noise from stratospheric laser data[J]. Proceedings of SPIE, 2003, 4891: 515-523.

[44] Wu S H, Liu Z S, Liu B Y. Enhancement of lidar backscatters signal-to-noise ratio using empirical mode decomposition method[J]. Optics Communications, 2006, 267(1): 137-144.

[45] 李利, 司锡才, 柴娟芳, 等. 基于重排小波-Radon变换的LFM雷达信号参数估计[J]. 系统工程与电子技术, 2009, 31(1): 74-77.

    Li L, Si X C, Chai J F, et al. Parameters estimation for LFM radar signal based on reassigned wavelet-Radon transform[J]. Systems Engineering and Electronics, 2009, 31(1): 74-77.

[46] Zhang YK, Ma XC, Hua DX, et al. An EMD-based denoising method for lidar signal[C]∥2010 3rd International Congress on Image and Signal Processing, October 16-18, 2010, Yantai, China. New York: IEEE, 2010: 4016- 4019.

[47] 陈冬, 王江安, 康圣. 脉冲激光雷达信号降噪方法对比[J]. 舰船科学技术, 2011, 33(4): 93-97.

    Chen D, Wang J A, Kang S. Comparison of backscattering lidar signal denoising methods[J]. Ship Science and Technology, 2011, 33(4): 93-97.

[48] 何俊峰, 刘文清, 张玉钧, 等. HHT在激光云高仪后向散射信号处理中的应用[J]. 红外与激光工程, 2012, 41(2): 397-403.

    He J F, Liu W Q, Zhang Y J, et al. New method of lidar ceilometer backscatter signal processing based on Hilbert-Huang transform[J]. Infrared and Laser Engineering, 2012, 41(2): 397-403.

[49] Stephenson JH, GreenwoodE. Effects of vehicle weight and true versus indicated airspeed on BVI noise during steady descending flight[C]∥71st Annual AHS Forum and Technology Display, May 5-7, 2015, Virginia Beach, VA, USA.2015.

[50] SaeedU, RocadenboschF, CrewellS. Adaptive estimation of the stable boundary-layer height using backscatter LiDAR data and a Kalman filter[C]∥2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), July 26-31, 2015, Milan, Italy. New York: IEEE, 2015: 3591- 3594.

[51] Zhang H Y, Lv T, Yan C H. The novel role of arctangent phase algorithm and voice enhancement techniques in laser hearing[J]. Applied Acoustics, 2017, 126: 136-142.

[52] 王玉峰, 曹小明, 张晶, 等. 基于小波去噪算法的全天时大气水汽拉曼激光雷达探测与分析[J]. 光学学报, 2018, 38(2): 0201001.

    Wang Y F, Cao X M, Zhang J, et al. Detection and analysis of all-day atmospheric water vapor Raman lidar based on wavelet denoising algorithm[J]. Acta Optica Sinica, 2018, 38(2): 0201001.

[53] Chang J H, Zhu L Y, Li H X, et al. Noise reduction in Lidar signal using correlation-based EMD combined with soft thresholding and roughness penalty[J]. Optics Communications, 2018, 407: 290-295.

[54] Olsson, A. Target recognition by vibrometry with a coherent laser radar: LITH-ISY-EX-3050-2003[R/OL].( 2003-05-13)[2018-05-01]. http:∥www.ep.liu.se/exjobb/isy/2003/3050/.

[55] AmzajerdianF, PierrottetD, Tolson RH, et al. Development of a coherent LiDAR for aiding precision soft landing on planetary bodies[C]∥13th Coherent Laser Radar Conference, October 16-21, 2005, Kamakura, Japan.2005: 20050240846.

[56] Falkowski M J. Smith A M S, Hudak A T, et al. Automated estimation of individual conifer tree height and crown diameter via two-dimensional spatial wavelet analysis of lidar data[J]. Canadian Journal of Remote Sensing, 2006, 32(2): 153-161.

[57] WeiH, BartelsM. Unsupervised segmentation using Gabor wavelets and statistical features in LIDAR data analysis[C]∥18th International Conference on Pattern Recognition, August 20-24, 2006, Hong Kong, China. New York: IEEE, 2006: 667- 670.

[58] van Gaalen J F, Kruse S E, Burroughs S M, et al. . Time-frequency methods for characterizing cuspate landforms in lidar data[J]. Journal of Coastal Research, 2009, 25(5): 1143-1148.

[59] Allen J D, Yuan J B, Liu X W, et al. A compressed sensing method with analytical results for lidar feature classification[J]. Proceedings of SPIE, 2011, 8055: 80550G.

[60] 何劲, 张群, 杨小优, 等. 逆合成孔径成像激光雷达成像算法[J]. 红外与激光工程, 2012, 41(4): 1094-1100.

    He J, Zhang Q, Yang X Y, et al. Imaging algorithm for inverse synthetic aperture imaging LADAR[J]. Infrared and Laser Engineering, 2012, 41(4): 1094-1100.

[61] Sobolev I, Babichenko S. Analysis of the performances of hyperspectral lidar for water pollution diagnostics[J]. EARSeL eProceedings, 2013, 12(2): 113-123.

[62] Deuge MD, QuadrosA, HungC, et al. Unsupervised feature learning for classification of outdoor 3D scans[C]∥Proceedings of the2013Australasian Conference on Robitics and Automation, December 2-4, 2013, University of New South Wales, Sydney Australia. Australian Robotics and Automation Association, 2013, N/A:98586.

[63] Wu YH, RuanH, Yu DB. Inverse synthetic aperture laser radar imaging algorithm for maneuvering targets[C]∥2014 7th International Congress on Image and Signal Processing (CISP), October 14-16, 2014, Dalian, China. New York: IEEE, 2014: 569- 574.

[64] Vercesi V, Onori D, Laghezza F, et al. Frequency-agile dual-frequency lidar for integrated coherent radar-lidar architectures[J]. Optics Letters, 2015, 40(7): 1358-1361.

[65] Konsoer K, Rhoads B, Best J, et al. Length scales and statistical characteristics of outer bank roughness for large elongate meander bends:the influence of bank material properties, floodplain vegetation and flow inundation[J]. Earth Surface Processes and Landforms, 2017, 42(13): 2024-2037.

[66] Wang N, Wang R, Mo D, et al. Inverse synthetic aperture LADAR demonstration: system structure, imaging processing, and experiment result[J]. Applied Optics, 2018, 57(2): 230.

[67] Youmans D G. Joint time-frequency transform processing for linear and sinusoidal FM coherent ladars[J]. Proceedings of SPIE, 2003, 5087: 46-57.

[68] 王学勤, 董艳群, 原帅, 等. 激光雷达微多普勒效应的仿真研究[J]. 激光技术, 2007, 31(2): 117-119,146.

    Wang X Q, Dong Y Q, Yuan S, et al. Study on simulation of micro-Doppler effect in lidar[J]. Laser Technology, 2007, 31(2): 117-119, 146.

[69] Gueguen P, Jolivet V, Michel C, et al. Comparison of velocimeter and coherent lidar measurements for building frequency assessment[J]. Bulletin of Earthquake Engineering, 2010, 8(2): 327-338.

[70] 何劲, 张群, 罗迎, 等. 逆合成孔径成像激光雷达微多普勒效应分析及特征提取[J]. 电子学报, 2011, 39(9): 2052-2059.

    He J, Zhang Q, Luo Y, et al. Analysis of micro-doppler effect and feature extraction of target in inverse synthetic aperture imaging ladar[J]. Acta Electronica Sinica, 2011, 39(9): 2052-2059.

[71] 朱丰, 张群, 冯有前, 等. 逆合成孔径激光雷达鸟类目标压缩感知识别方法[J]. 红外与激光工程, 2013, 42(1): 256-261.

    Zhu F, Zhang Q, Feng Y Q, et al. Compressed sensing identification approach for avian with inverse synthetic aperture lidar[J]. Infrared and Laser Engineering, 2013, 42(1): 256-261.

[72] Tahmoush D. Extracting and analyzing micro-Doppler from ladar signatures[J]. Proceedings of SPIE, 2015, 9461: 94611F.

[73] 王云鹏, 胡以华, 雷武虎, 等. 基于激光回波时频图纹理特征的飞机目标分类方法[J]. 光学学报, 2017, 37(11): 1128004.

    Wang Y P, Hu Y H, Lei W H, et al. Aircraft target classification method based on texture feature of laser echo time-frequency image[J]. Acta Optica Sinica, 2017, 37(11): 1128004.