• Laser & Optoelectronics Progress
  • Vol. 55, Issue 12, 120005 (2018)
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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    DOI: 10.3788/LOP55.120005 Cite this Article Set citation alerts
    Yanping Liu, Chong Wang, Haiyun Xia. Application Progress of Time-Frequency Analysis for Lidar[J]. Laser & Optoelectronics Progress, 2018, 55(12): 120005 Copy Citation Text show less
    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)
    Fig. 1. 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
    Fig. 2. 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
    Fig. 3. 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
    Fig. 4. 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
    Fig. 5. 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
    Fig. 6. 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
    Fig. 7. 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
    Fig. 8. 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
    Fig. 9. 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
    Fig. 10. 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
    Fig. 11. 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
    Fig. 12. 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
    Fig. 13. 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
    Fig. 14. 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
    Fig. 15. Spectrogram of walking person
    MethodBorn-JordanBinomialRichmanChoi-WilliamsQuasi-WignerPageRihaczek
    LO noise10.90.80.70.70.60.5
    Table 1. Approximate peak to LO noise performances for continuous wave coherent lidar
    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
    Table 2. Comparison of various time-frequency analysis methods
    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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