Journals > > Topics > Atmospheric and Oceanic Optics
Atmospheric and Oceanic Optics|35 Article(s)
Application of Deep Neural Network in Wavefront Sensing Based on Transport of Intensity Equation
Huizhe YANG, Haoran ZHANG, Jin LIU, Jing WAN... and Yonghui LIANG|Show fewer author(s)
The Transport of Intensity Equation (TIE) offers an effective method for wavefront sensing,utilizing the variations in near-field defocused intensity distribution patterns across multiple propagation distances to reconstruct the phase aberrations introduced by turbulent media,such as the atmosphere. YANG Huizhe et al have explored TIE-based wavefront sensing for satellite-ground laser communication systems,addressing challenges related to the Point-Ahead Angle (PAA). Their simulations and bench experiments,using a Zernike-based linear reconstruction method,demonstrated effectiveness under high Signal-to-Noise Ratio (SNR) conditions. However,linear wavefront reconstruction faces significant nonlinear errors,rendering it ineffective in low SNR environments,which are common in low laser power scenarios typical of laser communication systems.To address these challenges,this paper proposes a Deep Neural Network (DNN) training model. The model utilizes the differences in intensity distributions observed at two distinct propagation distances as the input data. The outputs of the model are the first 4 to 79 orders of the Zernike coefficients corresponding to the phase aberrations. The input and output data used for DNN training are simulated through two processes based on the actual satellite-ground laser communication systems. The first process is the uplink propagation of a collimated laser beam through the atmospheric turbulence,while the second process is the reimaging of the backscattered patterns from these different altitudes. To generate a diverse set of datasets,three variable parameter sets are employed: the atmospheric coherence lengths of 0.05,0.10,and 0.15 meters; the turbulence layer heights of 0,5,and 10 kilometers; and the laser powers of 5,10,20,50,100,200,and 300 watts. This results in 63 unique combinations. Each combination contains 10,000 random phase screens,yielding a total of 630,000 training data. By comparing the Wavefront Errors (WFE) between the original and reconstructed phases,different model architectures,loss functions,and optimizers are evaluated. Ultimately,ResNet34 is chosen as the backbone network. A linear weight pooling method is proposed for the neck network,along with the Weighted Mean Absolute Error (WMAE) function and the SophiaG optimizer.Simulation results provide compelling evidence that the DNN approach significantly outperforms the traditional linear reconstruction methods. Notably,it substantially reduces the laser power requirements essential for effective wavefront sensing. For instance,at a laser power level of 5 W,the reconstruction accuracy achieved by the DNN model matches that of linear methods operating at a substantially higher power of 200 W. Furthermore,as the laser power exceeds 20 W,the detection error for the DNN approach stabilizes at approximately 200 nm RMS,reaching the accuracy limits of the 79th order Zernike polynomial. Moreover,the execution time is also a crucial indicator of its practicality,especially in real-time adaptive optics systems. Testing one thousand datasets on a single PC with an NVIDIA A 5 000 GPU yielded a total processing time of 0.52 seconds for the DNN,resulting in an average processing time of approximately 0.52 milliseconds per dataset,thereby meeting the real-time requirements of adaptive optics systems with a KHz sampling frequency. In contrast,under the same hardware conditions the linear reconstruction method needs approximately 27.31 milliseconds per dataset. The DNN method is about 52 times faster than the linear reconstruction method,highlighting the significant advantages of DNNs in practical applications.Although the DNN method demonstrates excellent performance in wavefront sensing accuracy and execution efficiency,it still has some shortcomings. First,the reliance on training data is a common issue for DNNs. The performance of DNNs is highly dependent on the quality and diversity of the training data. If the actual turbulent conditions differ significantly from the training data,the model's performance can decline sharply. Therefore,it is necessary to further enhance the diversity of the training data to cover a broader range of turbulent conditions and noise levels. Second,the model's interpretability is limited. DNNs are often regarded as black boxes,making their internal decision-making processes difficult to explain using physical laws. In this paper,we designed the linear weight pooling and a weighted mean absolute error loss function based on the physical context of the task. However,further efforts are required to integrate DNNs with the physical models to improve the model's interpretability and robustness. The Transport of Intensity Equation (TIE) offers an effective method for wavefront sensing,utilizing the variations in near-field defocused intensity distribution patterns across multiple propagation distances to reconstruct the phase aberrations introduced by turbulent media,such as the atmosphere. YANG Huizhe et al have explored TIE-based wavefront sensing for satellite-ground laser communication systems,addressing challenges related to the Point-Ahead Angle (PAA). Their simulations and bench experiments,using a Zernike-based linear reconstruction method,demonstrated effectiveness under high Signal-to-Noise Ratio (SNR) conditions. However,linear wavefront reconstruction faces significant nonlinear errors,rendering it ineffective in low SNR environments,which are common in low laser power scenarios typical of laser communication systems.To address these challenges,this paper proposes a Deep Neural Network (DNN) training model. The model utilizes the differences in intensity distributions observed at two distinct propagation distances as the input data. The outputs of the model are the first 4 to 79 orders of the Zernike coefficients corresponding to the phase aberrations. The input and output data used for DNN training are simulated through two processes based on the actual satellite-ground laser communication systems. The first process is the uplink propagation of a collimated laser beam through the atmospheric turbulence,while the second process is the reimaging of the backscattered patterns from these different altitudes. To generate a diverse set of datasets,three variable parameter sets are employed: the atmospheric coherence lengths of 0.05,0.10,and 0.15 meters; the turbulence layer heights of 0,5,and 10 kilometers; and the laser powers of 5,10,20,50,100,200,and 300 watts. This results in 63 unique combinations. Each combination contains 10,000 random phase screens,yielding a total of 630,000 training data. By comparing the Wavefront Errors (WFE) between the original and reconstructed phases,different model architectures,loss functions,and optimizers are evaluated. Ultimately,ResNet34 is chosen as the backbone network. A linear weight pooling method is proposed for the neck network,along with the Weighted Mean Absolute Error (WMAE) function and the SophiaG optimizer.Simulation results provide compelling evidence that the DNN approach significantly outperforms the traditional linear reconstruction methods. Notably,it substantially reduces the laser power requirements essential for effective wavefront sensing. For instance,at a laser power level of 5 W,the reconstruction accuracy achieved by the DNN model matches that of linear methods operating at a substantially higher power of 200 W. Furthermore,as the laser power exceeds 20 W,the detection error for the DNN approach stabilizes at approximately 200 nm RMS,reaching the accuracy limits of the 79th order Zernike polynomial. Moreover,the execution time is also a crucial indicator of its practicality,especially in real-time adaptive optics systems. Testing one thousand datasets on a single PC with an NVIDIA A 5 000 GPU yielded a total processing time of 0.52 seconds for the DNN,resulting in an average processing time of approximately 0.52 milliseconds per dataset,thereby meeting the real-time requirements of adaptive optics systems with a KHz sampling frequency. In contrast,under the same hardware conditions the linear reconstruction method needs approximately 27.31 milliseconds per dataset. The DNN method is about 52 times faster than the linear reconstruction method,highlighting the significant advantages of DNNs in practical applications.Although the DNN method demonstrates excellent performance in wavefront sensing accuracy and execution efficiency,it still has some shortcomings. First,the reliance on training data is a common issue for DNNs. The performance of DNNs is highly dependent on the quality and diversity of the training data. If the actual turbulent conditions differ significantly from the training data,the model's performance can decline sharply. Therefore,it is necessary to further enhance the diversity of the training data to cover a broader range of turbulent conditions and noise levels. Second,the model's interpretability is limited. DNNs are often regarded as black boxes,making their internal decision-making processes difficult to explain using physical laws. In this paper,we designed the linear weight pooling and a weighted mean absolute error loss function based on the physical context of the task. However,further efforts are required to integrate DNNs with the physical models to improve the model's interpretability and robustness.
Acta Photonica Sinica
- Publication Date: Dec. 25, 2024
- Vol. 53, Issue 12, 1201001 (2024)
Application of Deep Learning in Underwater Imaging（Invited）
Jun XIE, Jianglei DI, and Yuwen QIN
Underwater imaging plays an increasingly important role in marine military, marine engineering, marine resource development,marine environmental protection, and so on, with the advantage of providing rich information, high resolution and high visibility underwater images. However, a large number of plankton and suspended particles in water environment, especially in the marine environment, causing strong scattering and absorption effects and resulting in image degradation problems such as blurring, short imaging distance, color distortion, low contrast, etc. Therefore, a series of underwater imaging methods have been proposed to solve the above problems.The underwater image enhancement technology can be used for image denoising, contrastimprovement and color distortioncorrection. The underwater image restoration uses the physical model of water degradation to restore the real image. The underwater polarization imaging uses the polarization difference between background and target to remove noise. The underwater ghost imaging and underwater compressed sensing imaging are used for imaging in scattering media. The underwater spectral imaging is used for color restoration. The underwater laser imaging is used for long-range and three-dimensional imaging. The underwater holographic imaging is used for water microorganism imaging, and so on. However, the above methods can only solve some image degradation problems, and there are some drawbacks, such as the subjectivity of underwater image enhancement technology, the dependence of underwater image recovery technology on prior information, and the computational load of underwater image correlation.The development of deep learning together with the development of hardware technology provides new solutions to the above problems, which makes the combination of deep learning and underwater imaging technology more and more widely used. As a powerful tool, neural network can extract similar features of different images using a wide range of datasets and convert them into high-level features, which can be used to process new input data, and completes a variety of complex tasks implicitly. It performs excellently in the field of image processing, and has made some achievements in the application of underwater imaging.Deep learning-basedimage restoration uses neural network to establish image-parameter mapping to estimate model parameters, avoiding human-dominant influence. Deep learning-based polarization imaging uses a neural network to map polarized images to clear images for image denoising. Deep learning-based spectral underwater imaging technology uses neural network to fuse multispectral images and hyperspectral images to obtain images with both high spatial resolution and hyperspectral resolution. However, some problems such as lack of datasets, poor generalization, and insufficient network interpretabilitystill exist, which need to be further solved.In this review, we discuss the characteristics of water environment and the various problems existing in underwater imaging, such as image blurring, short imaging distance, severe color distortion, and so on. The causes of the problem are analyzed and the underwater IFM model proposed by Jaffe-McGlamey is introduced. The latest application progress of various classic underwater imaging methods is systematically reviewed, including underwater image enhancement, underwater image restoration, underwater polarization imaging, underwater correlation imaging, underwater spectral imaging, underwater compression sensing imaging, underwater laser imaging and underwater holographic imaging. In addition, the basic concepts of deep learning, the composition of neural network and the structure of classical CNN network are introduced, and the latest application in combination with the above underwater imaging technology is systematically reviewed. At the same time, the application characteristics, deficiencies of traditional underwater imaging and the improvement by deep learning are analyzed and compared, and the applications of deep learning in various imaging methods are summarized. CNN network structure and MSE loss function are most commonly used due to its simplicity and efficiency. Finally, the future direction of underwater imaging technology based on deep learning is prospected. Underwater imaging plays an increasingly important role in marine military, marine engineering, marine resource development,marine environmental protection, and so on, with the advantage of providing rich information, high resolution and high visibility underwater images. However, a large number of plankton and suspended particles in water environment, especially in the marine environment, causing strong scattering and absorption effects and resulting in image degradation problems such as blurring, short imaging distance, color distortion, low contrast, etc. Therefore, a series of underwater imaging methods have been proposed to solve the above problems.The underwater image enhancement technology can be used for image denoising, contrastimprovement and color distortioncorrection. The underwater image restoration uses the physical model of water degradation to restore the real image. The underwater polarization imaging uses the polarization difference between background and target to remove noise. The underwater ghost imaging and underwater compressed sensing imaging are used for imaging in scattering media. The underwater spectral imaging is used for color restoration. The underwater laser imaging is used for long-range and three-dimensional imaging. The underwater holographic imaging is used for water microorganism imaging, and so on. However, the above methods can only solve some image degradation problems, and there are some drawbacks, such as the subjectivity of underwater image enhancement technology, the dependence of underwater image recovery technology on prior information, and the computational load of underwater image correlation.The development of deep learning together with the development of hardware technology provides new solutions to the above problems, which makes the combination of deep learning and underwater imaging technology more and more widely used. As a powerful tool, neural network can extract similar features of different images using a wide range of datasets and convert them into high-level features, which can be used to process new input data, and completes a variety of complex tasks implicitly. It performs excellently in the field of image processing, and has made some achievements in the application of underwater imaging.Deep learning-basedimage restoration uses neural network to establish image-parameter mapping to estimate model parameters, avoiding human-dominant influence. Deep learning-based polarization imaging uses a neural network to map polarized images to clear images for image denoising. Deep learning-based spectral underwater imaging technology uses neural network to fuse multispectral images and hyperspectral images to obtain images with both high spatial resolution and hyperspectral resolution. However, some problems such as lack of datasets, poor generalization, and insufficient network interpretabilitystill exist, which need to be further solved.In this review, we discuss the characteristics of water environment and the various problems existing in underwater imaging, such as image blurring, short imaging distance, severe color distortion, and so on. The causes of the problem are analyzed and the underwater IFM model proposed by Jaffe-McGlamey is introduced. The latest application progress of various classic underwater imaging methods is systematically reviewed, including underwater image enhancement, underwater image restoration, underwater polarization imaging, underwater correlation imaging, underwater spectral imaging, underwater compression sensing imaging, underwater laser imaging and underwater holographic imaging. In addition, the basic concepts of deep learning, the composition of neural network and the structure of classical CNN network are introduced, and the latest application in combination with the above underwater imaging technology is systematically reviewed. At the same time, the application characteristics, deficiencies of traditional underwater imaging and the improvement by deep learning are analyzed and compared, and the applications of deep learning in various imaging methods are summarized. CNN network structure and MSE loss function are most commonly used due to its simplicity and efficiency. Finally, the future direction of underwater imaging technology based on deep learning is prospected.
Acta Photonica Sinica
- Publication Date: Nov. 25, 2022
- Vol. 51, Issue 11, 1101001 (2022)
Investigation of Turbulence Parameters Based on Liquid-phase Cloud Microphysics Fluctuation Measured by Digital Holography
Pan GAO, Jun WANG, Jiabin TANG, Yangzi GAO... and Dengxin HUA|Show fewer author(s)
In response to the measurement requirements of turbulence in the study of cloud precipitation physics， a turbulence parameter characterization method based on digital holographic interferometry to measure the microphysical fluctuation of liquid cloud is proposed. Since there is no need to assume the distribution function of the cloud droplet spectrum and adjust related parameters， digital holographic interferometry can obtain the microphysical fluctuations of the liquid phase cloud affected by the actual turbulence. The fog droplets affected by steady turbulence are used to simulate liquid phase cloud droplets， and the droplet spectrum is recorded by a camera with a pixel size of 1.67 μm， and then the fluctuations of the water content and the average radius of the droplets are obtained. According to the theory of turbulence， the variance， time correlation coefficient， covariance and cross-correlation coefficient of turbulence are obtained. Finally， by analyzing the time correlation coefficients of water content at different intervals， the time scale of the turbulent field is 100 ms. Under the condition of a fixed sampling interval of 71 ms， the time correlation coefficient of water content at different initial times is analyzed， and the maximum deviation of the fluctuation from the average value is 23%， which proves that the flow field in the measurement area is steady turbulence. This method can provide an effective measurement method for studying the characteristics of liquid cloud microphysics and turbulence and the mechanism of their mutual influence. In response to the measurement requirements of turbulence in the study of cloud precipitation physics， a turbulence parameter characterization method based on digital holographic interferometry to measure the microphysical fluctuation of liquid cloud is proposed. Since there is no need to assume the distribution function of the cloud droplet spectrum and adjust related parameters， digital holographic interferometry can obtain the microphysical fluctuations of the liquid phase cloud affected by the actual turbulence. The fog droplets affected by steady turbulence are used to simulate liquid phase cloud droplets， and the droplet spectrum is recorded by a camera with a pixel size of 1.67 μm， and then the fluctuations of the water content and the average radius of the droplets are obtained. According to the theory of turbulence， the variance， time correlation coefficient， covariance and cross-correlation coefficient of turbulence are obtained. Finally， by analyzing the time correlation coefficients of water content at different intervals， the time scale of the turbulent field is 100 ms. Under the condition of a fixed sampling interval of 71 ms， the time correlation coefficient of water content at different initial times is analyzed， and the maximum deviation of the fluctuation from the average value is 23%， which proves that the flow field in the measurement area is steady turbulence. This method can provide an effective measurement method for studying the characteristics of liquid cloud microphysics and turbulence and the mechanism of their mutual influence.
Acta Photonica Sinica
- Publication Date: Jul. 25, 2021
- Vol. 50, Issue 7, 212 (2021)
Atmosphere Temperature Profiling and a Fusion Algorithm Based on Polarization HSRL and MWR
Jingjing LIU, Kailing LI, Zixiang XU, Jingzhe PANG... and Dengxin HUA|Show fewer author(s)
Atmospheric temperature is the basic parameter for the detection of atmospheric fine structure， and obtaining high-precision atmospheric temperature profile is crucial for weather forecast and climate research. In this paper， the self-developed polarization high-spectral-resolution lidar is used to realize the all-day and high signal-to-noise ratio measurement of atmospheric temperature. The algorithm by combining polarization high-spectral-resolution lidar and microwave radiometer are proposed by linear splicing method， and the complementary advantages of the two are realized. The results show that the polarization high-spectral-resolution lidar can realize the effective detection of atmospheric temperature at a distance of 4 km， and the error is mainly within ±2 K. The detection error of microwave radiometer is relatively low within 3 km， and the error is between -4 K and -2 K above 3 km. After splicing， the error is ±1 K within 3.5 km， and the correlation increases from 0.95 to 0.97. The results show that the lidar can effectively detect the atmospheric temperature in the boundary layer. Through the integration with the microwave radiometer， the blind area problem of the lidar can be solved， and the detection accuracy of the microwave radiometer can be improved. Atmospheric temperature is the basic parameter for the detection of atmospheric fine structure， and obtaining high-precision atmospheric temperature profile is crucial for weather forecast and climate research. In this paper， the self-developed polarization high-spectral-resolution lidar is used to realize the all-day and high signal-to-noise ratio measurement of atmospheric temperature. The algorithm by combining polarization high-spectral-resolution lidar and microwave radiometer are proposed by linear splicing method， and the complementary advantages of the two are realized. The results show that the polarization high-spectral-resolution lidar can realize the effective detection of atmospheric temperature at a distance of 4 km， and the error is mainly within ±2 K. The detection error of microwave radiometer is relatively low within 3 km， and the error is between -4 K and -2 K above 3 km. After splicing， the error is ±1 K within 3.5 km， and the correlation increases from 0.95 to 0.97. The results show that the lidar can effectively detect the atmospheric temperature in the boundary layer. Through the integration with the microwave radiometer， the blind area problem of the lidar can be solved， and the detection accuracy of the microwave radiometer can be improved.
Acta Photonica Sinica
- Publication Date: Jul. 25, 2021
- Vol. 50, Issue 7, 203 (2021)
Study on Simulation Method of Diffuse Radiation for Indoor Verification of Photoelectric Insolation Meter
Lingyun WANG, Yue MA, Haoyang LI, Guangxi LI... and Yuxin DU|Show fewer author(s)
Because the measurement accuracy of the diffuse radiation photoelectric sensor in the photoelectric insolation meter seriously affects the accuracy of the solar irradiance measurement， in order to reduce the scattering error，more accurate simulation of the diffuse radiation is needed for indoor calibration. Through the linear correlation analysis between the radiation attenuation rate and each meteorological factor in the day-by-day time scale， the daily horizontal solar scattering radiation model in Changchun area is obtained after multivariate linear regression fitting. The radiation attenuation caused by the scattering of water vapor and dust is simulated in the indoor verification system， and the indoor scattering environment simulation chamber is designed. The results show that the fitted values of the daily scattered radiation in the horizontal plane are well matched with the measured values. The measured values and fitted values are evenly distributed on both sides of the fitted line， and the correlation coefficient （R2） is above 0.8， indicating that the scattered radiation model has a good effect. Because the measurement accuracy of the diffuse radiation photoelectric sensor in the photoelectric insolation meter seriously affects the accuracy of the solar irradiance measurement， in order to reduce the scattering error，more accurate simulation of the diffuse radiation is needed for indoor calibration. Through the linear correlation analysis between the radiation attenuation rate and each meteorological factor in the day-by-day time scale， the daily horizontal solar scattering radiation model in Changchun area is obtained after multivariate linear regression fitting. The radiation attenuation caused by the scattering of water vapor and dust is simulated in the indoor verification system， and the indoor scattering environment simulation chamber is designed. The results show that the fitted values of the daily scattered radiation in the horizontal plane are well matched with the measured values. The measured values and fitted values are evenly distributed on both sides of the fitted line， and the correlation coefficient （R2） is above 0.8， indicating that the scattered radiation model has a good effect.
Acta Photonica Sinica
- Publication Date: May. 25, 2021
- Vol. 50, Issue 5, 233 (2021)
Performance Optimization and Experimental Research of Continuous Wave Coherent Wind Lidar
Wuhao YANG, Pu ZHANG, Xinfeng YANG, Qimin CHEN, and Wei ZHAO
Based on the requirement of wind velocity measurement， an all-fiber doppler coherent wind lidar system is built with continuous wave laser source at band 1 550 nm. It is analyzed theoretically for the Carrier-to-noise ratio function of the continuous wave coherent lidar and the weighing function of the wind velocity at different focusing distance on the basis of the lidar equation. A vari-focusing optical antenna with the focusing distance range from 5 m to 200 m is designed and fabricated according to the requirement of wind detection. The optical beam expanding module adopts the Galilean refractive structure with the beam expanding ratio as 23 and the optical quality is close to the diffraction limit. The calibration test is executed by using a rotating motor disk. The rotation speed range of the disk is from －3 000 r/min to +3 000 r/min. The diameter of the disk is 26 cm. While the doppler frequency shift of the line of sight velocity is positive and negative， the correlation coefficients between the velocity measurement data of the lidar system and the theoretical calculation result are 0.998 and 0.993. At the same time the standard deviations of velocity are 0.151 m/s and 0.229 m/s， respectively. The wind lidar is then used to measure the atmospheric wind speed. It works correctly to apply the wind lidar to measure the atmospheric wind velocity. Based on the requirement of wind velocity measurement， an all-fiber doppler coherent wind lidar system is built with continuous wave laser source at band 1 550 nm. It is analyzed theoretically for the Carrier-to-noise ratio function of the continuous wave coherent lidar and the weighing function of the wind velocity at different focusing distance on the basis of the lidar equation. A vari-focusing optical antenna with the focusing distance range from 5 m to 200 m is designed and fabricated according to the requirement of wind detection. The optical beam expanding module adopts the Galilean refractive structure with the beam expanding ratio as 23 and the optical quality is close to the diffraction limit. The calibration test is executed by using a rotating motor disk. The rotation speed range of the disk is from －3 000 r/min to +3 000 r/min. The diameter of the disk is 26 cm. While the doppler frequency shift of the line of sight velocity is positive and negative， the correlation coefficients between the velocity measurement data of the lidar system and the theoretical calculation result are 0.998 and 0.993. At the same time the standard deviations of velocity are 0.151 m/s and 0.229 m/s， respectively. The wind lidar is then used to measure the atmospheric wind speed. It works correctly to apply the wind lidar to measure the atmospheric wind velocity.
Acta Photonica Sinica
- Publication Date: Apr. 25, 2021
- Vol. 50, Issue 4, 81 (2021)
Research on Influence of Aberration and Turbulence on Performance of 90° Space Optical Hybrid
Xiyu GONG, Peng ZHANG, Xiaojie WU, Hang NAN... and Shoufeng TONG|Show fewer author(s)
The optical signal is distorted in the atmospheric turbulence， after passing through the defective optical antenna and the space optical hybrid， there are problems of low hybrid efficiency and jitter. According to the influence of 90° space optical hybrid with space output and single-mode output， the hybrid efficiency model of space optical hybrid with primary aberration is derived. Then the influence of aberration and turbulence on hybrid efficiency is studied. The simulation results show that for the space output 90° space optical hybrid with a target radius of 50 μm of the detector， the tilt， spherical aberration， defocus，coma，and astigmatism with aberration of 0.2λ cause the hybrid efficiency to decrease by 9.8%， 0.6%， 0.36%， 0.02% and 0.01%， respectively. Astigmatism and coma have no effect on the hybrid efficiency. For the single-mode output hybrid， the tilt， astigmatism， defocus， coma， and spherical aberration with aberration of 0.2λ cause the hybrid efficiency to decrease by 14.11%， 8.39%， 6.35%， 2.63% and 1.13%， respectively. When the turbulence intensity Cn2-17 m-2/3， the hybrid efficiency of the spatial output type is more than 0.19 higher than that of the single-mode output 90° space optical hybrid， and when the turbulence intensity Cn2>6.4×10-16 m-2/3 ， the hybrid efficiency of the two is close to zero. Finally， the space output and single-mode output optical hybrid are designed and processed， and the performance test platform is built. For the space output hybrid with a detector target surface radius of 50 μm， the tilt， spherical aberration， and defocus of 0.2λ cause the hybrid efficiency to decrease by 52%， 10%， and 6%； for the single-mode output hybrid， the tilt， astigmatism and defocus with aberration of 0.2λ cause the hybrid efficiency to decrease by 65%， 24% and 11%. Other aberrations have no effect on the hybrid efficiency.The experimental results of turbulence on the hybrid performance show that the standard deviation of the intermediate frequency signal value of the space output optical hybrid is 21.388， which is much lower than that of single-mode output standard deviation 247.442. The optical signal is distorted in the atmospheric turbulence， after passing through the defective optical antenna and the space optical hybrid， there are problems of low hybrid efficiency and jitter. According to the influence of 90° space optical hybrid with space output and single-mode output， the hybrid efficiency model of space optical hybrid with primary aberration is derived. Then the influence of aberration and turbulence on hybrid efficiency is studied. The simulation results show that for the space output 90° space optical hybrid with a target radius of 50 μm of the detector， the tilt， spherical aberration， defocus，coma，and astigmatism with aberration of 0.2λ cause the hybrid efficiency to decrease by 9.8%， 0.6%， 0.36%， 0.02% and 0.01%， respectively. Astigmatism and coma have no effect on the hybrid efficiency. For the single-mode output hybrid， the tilt， astigmatism， defocus， coma， and spherical aberration with aberration of 0.2λ cause the hybrid efficiency to decrease by 14.11%， 8.39%， 6.35%， 2.63% and 1.13%， respectively. When the turbulence intensity Cn2-17 m-2/3， the hybrid efficiency of the spatial output type is more than 0.19 higher than that of the single-mode output 90° space optical hybrid， and when the turbulence intensity Cn2>6.4×10-16 m-2/3 ， the hybrid efficiency of the two is close to zero. Finally， the space output and single-mode output optical hybrid are designed and processed， and the performance test platform is built. For the space output hybrid with a detector target surface radius of 50 μm， the tilt， spherical aberration， and defocus of 0.2λ cause the hybrid efficiency to decrease by 52%， 10%， and 6%； for the single-mode output hybrid， the tilt， astigmatism and defocus with aberration of 0.2λ cause the hybrid efficiency to decrease by 65%， 24% and 11%. Other aberrations have no effect on the hybrid efficiency.The experimental results of turbulence on the hybrid performance show that the standard deviation of the intermediate frequency signal value of the space output optical hybrid is 21.388， which is much lower than that of single-mode output standard deviation 247.442.
Acta Photonica Sinica
- Publication Date: Apr. 25, 2021
- Vol. 50, Issue 4, 66 (2021)
Measurement and Concentration Inversion of Ozone in Golmud by Laser Heterodyne Spectrometer
Jun HUANG, Yinbo HUANG, Xingji LU, Zhensong CAO... and Zihao YUAN|Show fewer author(s)
In order to measure the ozone concentration， a 3.3 μm laser heterodyne spectrometer for field observation was built and the spectral resolution was measured to be 0.004 cm-1. The ozone spectrum was measured at Golmud in Qinghai Province by the 3.3 μm laser heterodyne spectrometer， and the ozone concentration was inversed by using the optimal estimation algorithm. During the field observation， the average column concentration of ozone was inversed to be 241.7 DU， the column concentration increased about 4 DU/h. The results show that the laser heterodyne spectrometer combines with the optimal estimation algorithm is capable of measuring the ozone concentration of the whole atmosphere at the plateau， and has important applications for the environment， meteorology and laser atmospheric transmission assessment. In order to measure the ozone concentration， a 3.3 μm laser heterodyne spectrometer for field observation was built and the spectral resolution was measured to be 0.004 cm-1. The ozone spectrum was measured at Golmud in Qinghai Province by the 3.3 μm laser heterodyne spectrometer， and the ozone concentration was inversed by using the optimal estimation algorithm. During the field observation， the average column concentration of ozone was inversed to be 241.7 DU， the column concentration increased about 4 DU/h. The results show that the laser heterodyne spectrometer combines with the optimal estimation algorithm is capable of measuring the ozone concentration of the whole atmosphere at the plateau， and has important applications for the environment， meteorology and laser atmospheric transmission assessment.
Acta Photonica Sinica
- Publication Date: Apr. 25, 2021
- Vol. 50, Issue 4, 57 (2021)
Comparative Analysis of Metastable Helium Resonance Fluorescence Lidar Systems
Jiaxin LAN, Ruocan ZHAO, Tingyu PAN, Xianghui XUE... and Zimu LI|Show fewer author(s)
The pulsed lidar system and continuous lidar system are improved by redesigning the parameters considering more details of the systems and choosing the more appropriate equipment according to limitations of the systems. After the simulation of the return signals， these two kinds of lidar systems are compared in SNR and difficulty of implementation. The results show that the pulsed lidar is more suitable for measuring the metastable Helium density of thermosphere and exosphere. The pulsed lidar system and continuous lidar system are improved by redesigning the parameters considering more details of the systems and choosing the more appropriate equipment according to limitations of the systems. After the simulation of the return signals， these two kinds of lidar systems are compared in SNR and difficulty of implementation. The results show that the pulsed lidar is more suitable for measuring the metastable Helium density of thermosphere and exosphere.
Acta Photonica Sinica
- Publication Date: Apr. 25, 2021
- Vol. 50, Issue 4, 46 (2021)
Observation of HCHO before and after Epidemic in Huaibei Area Based on MAX-DOAS
Hexiang QI, Yingying GUO, Fusheng MOU, and Suwen LI
A ground-based Multi-Axis Differential Optical Absorption Spectroscopy （MAX-DOAS） with the characteristics of simple operation， wide range and high sensitivity was constructed， and the time series of HCHO in Huaibei area from October 2019 to May 2020 were obtained continuously. In order to reduce the interference of other gases， different bands are used to retrieve the differential slant column density of HCHO. Through comparison， it was found that when the band of 324~342 nm was selected， the inversion error fluctuation is the minimum， and the concentration of HCHO gas can be obtained precisely. According to the results of HCHO monthly mean values， compared with the before and after COVIN-9 epidemic ，the concentration of HCHO in the COVIN-9 epidemic were decreased by 35% and 23% respectively. The results of daily and weekly variations respectively showed that HCHO concentration in Huaibei area have daily variation characteristics of high in the morning and evening and low in noon， and there is no obvious weekend effect. Combined with Hysplit wind field backward trajectory model， the wind field of high value weather was studied. It was found that during January 12~14 and 18~21， 2020， under the influence of northwest wind field， the pollution transport from Dangshan and other places in Huaibei area will be affected， which will lead to the increase of HCHO concentration. The result of MAX-DOAS measurement of HCHO vertical column density were compared with the OMI satellite data， and it was found that the two methods had good consistency （R2 = 0.87）. A ground-based Multi-Axis Differential Optical Absorption Spectroscopy （MAX-DOAS） with the characteristics of simple operation， wide range and high sensitivity was constructed， and the time series of HCHO in Huaibei area from October 2019 to May 2020 were obtained continuously. In order to reduce the interference of other gases， different bands are used to retrieve the differential slant column density of HCHO. Through comparison， it was found that when the band of 324~342 nm was selected， the inversion error fluctuation is the minimum， and the concentration of HCHO gas can be obtained precisely. According to the results of HCHO monthly mean values， compared with the before and after COVIN-9 epidemic ，the concentration of HCHO in the COVIN-9 epidemic were decreased by 35% and 23% respectively. The results of daily and weekly variations respectively showed that HCHO concentration in Huaibei area have daily variation characteristics of high in the morning and evening and low in noon， and there is no obvious weekend effect. Combined with Hysplit wind field backward trajectory model， the wind field of high value weather was studied. It was found that during January 12~14 and 18~21， 2020， under the influence of northwest wind field， the pollution transport from Dangshan and other places in Huaibei area will be affected， which will lead to the increase of HCHO concentration. The result of MAX-DOAS measurement of HCHO vertical column density were compared with the OMI satellite data， and it was found that the two methods had good consistency （R2 = 0.87）.
Acta Photonica Sinica
- Publication Date: Jan. 25, 2021
- Vol. 50, Issue 1, 210 (2021)
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