Simulation and Analysis of Mie-Scattering Lidar-Measuring Atmospheric Turbulence Profile

Lu, Yuqing; Mao, Jiandong; Zhang, Yingnan; Zhao, Hu; Zhou, Chong; Gong, Xin; Wang, Qiang; Zhang, Yi

doi:10.3390/s22062333

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…In summary, the atmospheric turbulence detection method adopted is as follows: based on RTS theory, first, the scintillation index

at each distance is obtained according to Equation (2); then, the variation trend of

and

along the propagation path is obtained according to Equation (5), and finally, the atmospheric turbulence profile can be obtained. Before the experiment, we carried out some systematic simulation studies to ensure the feasibility of the experimental scheme and the selection of system parameters [ 30 , 31 ].…”

Section: Principle Of Detectionmentioning

confidence: 99%

Novel Detection of Atmospheric Turbulence Profile Using Mie-Scattering Lidar Based on Non-Kolmogorov Turbulence Theory

Mao

Zhang

et al. 2023

Entropy

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Turbulence can cause effects such as light intensity fluctuations and phase fluctuations when a laser is transmitted in the atmosphere, which has serious impacts on a number of optical engineering application effects and on climate improvement. Therefore, accurately obtaining real-time turbulence intensity information using lidar-active remote sensing technology is of great significance. In this paper, based on residual turbulent scintillation theory, a Mie-scattering lidar method was developed to detect atmospheric turbulence intensity. By extracting light intensity fluctuation information from a Mie-scattering lidar return signal, the atmospheric refractive index structure constant, Cn2, representing the atmospheric turbulence intensity, could be obtained. Specifically, the scintillation effect on the detection path was analyzed, and the probability density distribution of the light intensity of the Mie-scattering lidar return signal was studied. It was verified that the probability density of logarithmic light intensity basically follows a normal distribution under weak fluctuation conditions. The Cn2 profile based on Kolmogorov turbulence theory was retrieved using a layered, iterative method through the scintillation index. The method for detecting Kolmogorov turbulence intensity was applied to the detection of the non-Kolmogorov turbulence intensity. Through detection using the scintillation index, the corresponding C˜n2 profile could be calculated. The detection of the C˜n2 and Cn2 profiles were compared with the Hufnagel–Valley (HV) night model in the Yinchuan area. The results show that the detection results are consistent with the overall change trend of the model. In general, it is feasible to detect a non-Kolmogorov turbulence profile using Mie-scattering lidar.

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“…In summary, the atmospheric turbulence detection method adopted is as follows: based on RTS theory, first, the scintillation index

at each distance is obtained according to Equation (2); then, the variation trend of

and

Section: Principle Of Detectionmentioning

confidence: 99%

Novel Detection of Atmospheric Turbulence Profile Using Mie-Scattering Lidar Based on Non-Kolmogorov Turbulence Theory

Mao

Zhang

et al. 2023

Entropy

Self Cite

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Real time simulation of atmospheric turbulence based on GPU

Yin,

Ni,

et al. 2024

Infrared Physics & Technology

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Rapidly Measuring Scattered Polarization Parameters of the Individual Suspended Particle with Continuously Large Angular Range

Chen

Wang

et al. 2022

Biosensors

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Suspended particles play a vital role in aquatic environments. We propose a method to rapidly measure the scattered polarization parameters of individual suspended particles with continuously large angular range (PCLAR), from 60° to 120° in one shot. A conceptual setup is built to measure PCLAR with 20 kHz; to verify the setup, 10 μm-diameter silica microspheres suspended in water, whose PCLAR are consistent with those simulated by Mie theory, are measured. PCLAR of 6 categories of particles are measured, which enables high-accuracy classification with the help of a convolutional neural network algorithm. PCLAR of different mixtures of Cyclotella stelligera and silica microspheres are measured to successfully identify particulate components. Furthermore, classification ability comparisons of different angular-selection strategies show that PCLAR enables the best classification beyond the single angle, discrete angles and small-ranged angles. Simulated PCLAR of particles with different size, refractive index, and structure show explicit discriminations between them. Inversely, the measured PCLAR are able to estimate the effective size and refractive index of individual Cyclotella cells. Results demonstrate the method’s power, which intrinsically takes the advantage of the optical polarization and the angular coverage. Future prototypes based on this concept would be a promising biosensor for particles in environmental monitoring.

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Simulation and Analysis of Mie-Scattering Lidar-Measuring Atmospheric Turbulence Profile

Cited by 3 publications

References 15 publications

Novel Detection of Atmospheric Turbulence Profile Using Mie-Scattering Lidar Based on Non-Kolmogorov Turbulence Theory

Novel Detection of Atmospheric Turbulence Profile Using Mie-Scattering Lidar Based on Non-Kolmogorov Turbulence Theory

Real time simulation of atmospheric turbulence based on GPU

Rapidly Measuring Scattered Polarization Parameters of the Individual Suspended Particle with Continuously Large Angular Range

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