激光与光电子学进展, 2018, 55 (12): 120005, 网络出版: 2019-08-01   

时频分析在激光雷达中的应用进展 下载: 886次

Application Progress of Time-Frequency Analysis for Lidar
作者单位
中国科学技术大学地球和空间科学学院, 安徽 合肥 230026
图 & 表

图 1. 尾涡仿真结果和径向廓线的Wigner-Ville分布。(a)尾涡对中径向风速的等高线数值模拟图;(b)三条视线的径向风速廓线图;(c)图(b)中黑色实线的平均Wigner-Ville分布

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)

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图 2. 风速随距离变化的谱图图像。(a)1.5 μm全光纤单频激光雷达;(b)长距多普勒测风雷达

Fig. 2. Spectral images of wind speed varying with distance. (a) 1.5 μm all-fiber single frequency lidar; (b) long-distance Doppler lidar

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图 3. 二维小波重建结果。(a) 2014年7月16至18日相对温度扰动;(b)重建周期为3.6 h;(c)重建周期为4.8 h;(d)重建周期为7.8 h;(e)结合三个主小波重建的温度扰动场

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

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图 4. 重力波扰动(a)~(c)和小波谱能量分布函数(d)~(f)。(a)原始温度扰动;(b)相位上行的波;(c)相位下行的波;(d)垂直波长与相速度;(e)垂直波长与周期;(f)高度与垂直波长

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

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图 5. 模拟风切变与实测结果对比。(a)模拟结果;(b)实测

Fig. 5. Comparison of wind shear distribution between simulation results and actual measurements. (a) Simulation results; (b) actual measurements

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图 6. 反演结果对比图。(a)原始数据和去噪信号;(b)去噪数据和1000组累加信号的平均值

Fig. 6. Comparison diagrams of inversion results. (a) Original and denoised data; (b) denoised data and average of 1000 sets of accumulative signals

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图 7. 后向散射信号的谱图分布

Fig. 7. Spectral distribution of backscatter signals

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图 8. 谱图分析结果对比。(a)原始LDV谱图与波形图;(b) Wiener滤波后的信号谱图与波形图;(c)纯净信号的谱图与波形图

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

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图 9. 2016-09-22T00:00—2016-09-23T00:00,水汽混合比时间高度计(THI)图去噪前后结果显示图。 (a)去噪前; (b)去噪后

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

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图 10. 距离250 m处的目标回波信号谱图分析。(a)静目标;(b)动目标

Fig. 10. Spectrograms of the received signals from the targets at 250 m. (a) Stationary target; (b) moving target

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图 11. Gabor小波变换测试结果。(a)原始数据1;(b)原始数据1分割结果;(c)原始数据2;(d)原始数据2分割结果

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

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图 12. 匹配追踪法分离树木和建筑物结果对比。(a)树木;(b)建筑物;(c)采用11×11窗检测到的树木区域;(d)采用11×11窗测到的建筑物区域;(e)采用7×7窗探测到的树木区域;(f)采用7×7窗探测到的建筑物区域

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

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图 13. 谱图结果。(a)当纵模间隔为10 GHz时目标风速的归一化谱图;(b)硬阈值处理后的风速谱图

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

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图 14. 飞机模型以及基于两种方法的对比图。(a)石制飞机模型的光学图;(b) FFT(快速傅里叶变换)方法成像结果图;(c) FFT方法方位多视图;(d) JTFT方法方位多视图

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

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图 15. 步行者谱图

Fig. 15. Spectrogram of walking person

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表 1连续光相干激光雷达LO噪声峰值性能

Table1. Approximate peak to LO noise performances for continuous wave coherent lidar

MethodBorn-JordanBinomialRichmanChoi-WilliamsQuasi-WignerPageRihaczek
LO noise10.90.80.70.70.60.5

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表 2各种时频方法对比

Table2. 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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刘燕平, 王冲, 夏海云. 时频分析在激光雷达中的应用进展[J]. 激光与光电子学进展, 2018, 55(12): 120005. 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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