Discrete spectral peak estimation in incoherent backscatter heterodyne lidar. II. Correlogram accumulation

Rye, Barry J.; Hardesty, R. Michael

doi:10.1109/36.210441

Cited by 81 publications

(23 citation statements)

References 12 publications

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“…This CRB can be evaluated numerically, and is found to agree with the analytical expression in case of only additive noise [13]: (15) in which CNR wf is the CNR taken in terms of waveform energy: CNR wf = E waveform /(PSD noise .t p ) (E waveform : waveform energy, PSD noise : noise power spectral density).…”

Section: Performance Of the ML Estimatormentioning

confidence: 62%

“…But the spectrogram can also be processed using a spectral equivalent of the phase based estimator originally given by Lee, as explained in [13,14]. Lee's Spectral Matching (LSM) consists in finding the best match in position between a reference spectrum S ref (f) (Gaussian spectrum induced by the speckle noise) and the short-term spectra composing the spectrogram.…”

Section: If Estimators In Cw Modementioning

confidence: 99%

“…ML is the equivalent of Levin's own maximum likelihood estimator developed for CW laser radar, explained in [13], but applied to poly-pulse waveforms. As Levin's estimator does in the spectral domain, when the phase modulation of the signal is suppressed assuming the instantaneous frequency was f, this likelihood quantifies how much the result is close to having the same covariance matrix as the supposedly remaining multiplicative and additive noises.…”

Section: Maximum Likelihood If Estimationmentioning

confidence: 99%

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Advanced signal processing methods for pulsed laser vibrometry

Totems¹,

Jolivet²,

Ovarlez³

et al. 2010

Appl. Opt.

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Although pulsed coherent laser radar vibrometry has been introduced as an improvement over its Continuous Wave (CW) counterpart, it remains very sensitive to decorrelation noises such as speckle, and other disturbances of the measurement. Taking advantage of more polyvalent poly-pulse waveforms, we address the issue with advanced signal processing. We have conducted what we believe is the first extensive comparison of processing techniques considering CW, pulse-pair and poly-pulse emissions. In this framework, we introduce a computationally efficient maximum likelihood estimator and test signal tracking on pseudo time-frequency representations, which respectively help deal with speckle noise and fading of the signal, in harsh noise conditions. Our comparison on simulated signals is validated on a 1.55µm all-fiber vibrometer experiment, with an apparatus simulating vibration and strong speckle noise. Results show the advantage of estimators taking into account actual noise statistics, and call for a wider use of timefrequency representations to track the vibration modulated signal. OCIS codes: 280.0280, 280.3340, 120.7280. 2

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Section: Performance Of the ML Estimatormentioning

confidence: 62%

Section: If Estimators In Cw Modementioning

confidence: 99%

Section: Maximum Likelihood If Estimationmentioning

confidence: 99%

See 1 more Smart Citation

Advanced signal processing methods for pulsed laser vibrometry

Totems¹,

Jolivet²,

Ovarlez³

et al. 2010

Appl. Opt.

View full text Add to dashboard Cite

show abstract

“…To assess accuracy of the velocity estimates the theoretical standard deviation for each measurement given by Rye and Hardesty (1993) was used…”

Section: Doppler Lidar Observationsmentioning

confidence: 99%

Boundary layer dynamics over London, UK, as observed using Doppler lidar during REPARTEE-II

et al. 2011

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Abstract. Urban boundary layers (UBLs) can be highly complex due to the heterogeneous roughness and heating of the surface, particularly at night. Due to a general lack of observations, it is not clear whether canonical models of boundary layer mixing are appropriate in modelling air quality in urban areas. This paper reports Doppler lidar observations of turbulence profiles in the centre of London, UK, as part of the second REPARTEE campaign in autumn 2007. Lidar-measured standard deviation of vertical velocity averaged over 30 min intervals generally compared well with in situ sonic anemometer measurements at 190 m on the BT telecommunications Tower. During calm, nocturnal periods, the lidar underestimated turbulent mixing due mainly to limited sampling rate. Mixing height derived from the turbulence, and aerosol layer height from the backscatter profiles, showed similar diurnal cycles ranging from c. 300 to 800 m, increasing to c. 200 to 850 m under clear skies. The aerosol layer height was sometimes significantly different to the mixing height, particularly at night under clear skies. For convective and neutral cases, the scaled turbulence profiles resembled canonical results; this was less clear for the stable case. Lidar observations clearly showed enhanced mixing beneath stratocumulus clouds reaching down on occasion to approximately half daytime boundary layer depth. OnCorrespondence to: J. F. Barlow (j.f.barlow@reading.ac.uk) one occasion the nocturnal turbulent structure was consistent with a nocturnal jet, suggesting a stable layer. Given the general agreement between observations and canonical turbulence profiles, mixing timescales were calculated for passive scalars released at street level to reach the BT Tower using existing models of turbulent mixing. It was estimated to take c. 10 min to diffuse up to 190 m, rising to between 20 and 50 min at night, depending on stability. Determination of mixing timescales is important when comparing to physicochemical processes acting on pollutant species measured simultaneously at both the ground and at the BT Tower during the campaign. From the 3 week autumnal data-set there is evidence for occasional stable layers in central London, effectively decoupling surface emissions from air aloft.

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“…This value is somewhat larger than the threshold of −20 dB used by Barlow et al (2011) based on the considerations of Rye and Hardesty (1993). For the instrument used in this study, the value SNR ts indicates the SNR range below which the Doppler velocity consists only of white noise.…”

Section: Wind Lidarmentioning

confidence: 76%

Mixing-layer height retrieval with ceilometer and Doppler lidar: from case studies to long-term assessment

Hirsikko²,

et al. 2014

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Abstract. Aerosol signatures observed by ceilometers are frequently used to derive mixing-layer height (MLH) which is an essential variable for air quality modelling. However, Doppler wind lidar measurements of vertical velocity can provide a more direct estimation of MLH via simple thresholding. A case study reveals difficulties in the aerosol-based MLH retrieval during transition times when the mixing layer builds up in the morning and when turbulence decays in the afternoon. The difficulties can be explained by the fact that the aerosol distribution is related to the history of the mixing process and aerosol characteristics are modified by humidification. The results of the case study are generalized by evaluating one year of joint measurements by a Vaisala CT25K and a HALO Photonics Streamline wind lidar. On average the aerosol-based retrieval gives higher MLH than the wind lidar with an overestimation of MLH by about 300 m (600 m) in the morning (late afternoon). Also, the daily aerosol-based maximum MLH is larger and occurs later during the day and the average morning growth rates are smaller than those derived from the vertical wind. In fair weather conditions classified by less than 4 octa cloud cover the mean diurnal cycle of cloud base height corresponds well to the mixing-layer height showing potential for a simplified MLH estimation.

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Discrete spectral peak estimation in incoherent backscatter heterodyne lidar. II. Correlogram accumulation

Cited by 81 publications

References 12 publications

Advanced signal processing methods for pulsed laser vibrometry

Advanced signal processing methods for pulsed laser vibrometry

Boundary layer dynamics over London, UK, as observed using Doppler lidar during REPARTEE-II

Mixing-layer height retrieval with ceilometer and Doppler lidar: from case studies to long-term assessment

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