Laser & Optoelectronics Progress, Volume. 55, Issue 12, 120005(2018)

Application Progress of Time-Frequency Analysis for Lidar

Yanping Liu*, Chong Wang**, and Haiyun Xia***
Author Affiliations
  • School of Earth and Space Science, University of Science and Technology of China, Hefei, Anhui 230026, China
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    Figures & Tables(17)
    Simulation results of tail vortex and Wigner-Ville distribution of radial velocity profiles. (a) Numerical simulation diagram of contour plot of tail vortex pair; (b) three radial velocity profiles of line of slight; (c) average Wigner-Ville distribution of black solid lines in fig. (b)
    Spectral images of wind speed varying with distance. (a) 1.5 μm all-fiber single frequency lidar; (b) long-distance Doppler lidar
    Reconstruction results of 2D wavelet. (a) Original relative temperature perturbations from July 16 to 18, 2014; (b) reconstruction period of 3.6 h; (c) reconstruction period of 4.8 h; (d) reconstruction period of 7.8 h; (e) the temperature perturbation field reconstructed from combining the above three major wave packets
    Gravity wave perturbations (a)-(c) and distribution function of spectral energy (d)-(f). (a) Initial temperature perturbations; (b) waves with upward phase progression; (c) waves with downward phase progression; (d) Vertical wavelength versus phase velocity; (e) vertical wavelength versus period; (f) altitude versus vertical wavelength
    Comparison of wind shear distribution between simulation results and actual measurements. (a) Simulation results; (b) actual measurements
    Comparison diagrams of inversion results. (a) Original and denoised data; (b) denoised data and average of 1000 sets of accumulative signals
    Spectral distribution of backscatter signals
    Comparison of the spectrogram results. (a) Spectrogram and oscillogram of an original LDV signal; (b) spectrogram and oscillogram of a Wiener filtered signal; (c) spectrogram and oscillogram of a clean signal
    THI displays of water-vapor mixing ratio recorded from 2016-09-22T00:00 to 2016-09-23T00:00 before and after denosing. (a) Before denoising; (b) after denoising
    Spectrograms of the received signals from the targets at 250 m. (a) Stationary target; (b) moving target
    Test results of Gabor wavelet transform. (a) Tile 1 original data; (b) Tile 1 segmented result; (c) Tile 2 original data; (d) Tile 2 segmented result
    Comparison of segmented trees and buildings using matching pursuit method. (a) Trees; (b) buildings; (c) tree area detected by an 11×11 window; (d) building area detected by an 11×11 window; (e) tree area detected by a 7×7 window; (f) building area detected by a 7×7 window
    Spectrogram results. (a) Normalized spectrogram of the target speed versus time with tone spacing of 10 GHz; (b) velocity spectrogram after hard threshold processing
    Airplane model and imaging results based on two methods. (a) Optical photo of the airplane model made of stone; (b) image result based on the FFT(fast Fourier transformation) method; (c) azimuth multilook result based on the FFT method; (d) azimuth multilook result based on the JTFT method
    Spectrogram of walking person
    • Table 1. Approximate peak to LO noise performances for continuous wave coherent lidar

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      Table 1. Approximate peak to LO noise performances for continuous wave coherent lidar

      MethodBorn-JordanBinomialRichmanChoi-WilliamsQuasi-WignerPageRihaczek
      LO noise10.90.80.70.70.60.5
    • Table 2. Comparison of various time-frequency analysis methods

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      Table 2. Comparison of various time-frequency analysis methods

      CategoryMethodAdvantageWeakness
      Lineartime-frequencyrepresentationShort timeFouriertransformFree from cross-terms,fast implementation,physically meaningfulLacks adaptability due tofixed window, limitedtime-frequency resolution
      WavelettransformFree from cross-terms,adaptive representation,effective in detecting transientsDifficult to selectwavelet basis, limitedtime-frequency resolution
      Bilineartime-frequencydistributionWigner-VilledistributionHightime-frequencyresolutionSuffers from cross-terminterference formulti-component signals
      CohenclassdistributionSuppressedcross-termsSuppression ofcross-terms can lead toreduced time-frequency resolution
      AffineclassdistributionSuppressedcross-termsSuppression of cross-termscan lead to reducedtime-frequency resolution
      ReassigneddistributionSuppressedcross-terms, improvedtime-frequency resolutionIneffective attime-frequency locations ofzero energy distribution
      AdaptiveoptimalkernelSuppressed crossterms, improvedtime-frequency resolutionHigh computationalcomplexity due tooptimization
      Adaptivenon-parametrictime-frequencyrepresentationHilbert-HuangtransformHigh time-frequency resolution,adaptive signal decompositionDifficult to resolve signalcomponents when instantaneousfrequencies have crossingson time-frequency plane,pseudo IMFs due to endpointeffects and intermittency
      Adaptiveparametrictime-frequencyrepresentationAdaptiveGaussianrepresentationSuppressedcross-terms, improvedtime-frequency resolutionHigh computationalcomplexity for search
      MatchingpursuitFree from cross-terms,adaptive representation ofcomplicated signalsRelies on dictionary,needs a priori knowledge toconstruct dictionary, highcomputational complexity due tooptimization in signal decomposition
      AdaptivechirpletdecompositionSuppressedcross-termsNeeds a priori knowledge,high computational complexitydue to optimization insignal decomposition
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    Yanping Liu, Chong Wang, Haiyun Xia. Application Progress of Time-Frequency Analysis for Lidar[J]. Laser & Optoelectronics Progress, 2018, 55(12): 120005

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    Paper Information

    Category: Reviews

    Received: May. 1, 2018

    Accepted: Jul. 5, 2018

    Published Online: Aug. 1, 2019

    The Author Email: Yanping Liu (yanping@mail.ustc.edu.cn), Chong Wang (wcltr@163.com), Haiyun Xia (hsia@ustc.edu.cn)

    DOI:10.3788/LOP55.120005

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