Wavefront distortion prediction method based on motion estimation

Zhou Xin; Li Xinyang

doi:10.12086/oee.2021.210288

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Zhou X, Li X Y. Wavefront distortion prediction method based on motion estimation[J]. Opto-Electron Eng, 2021, 48(10): 210288. doi: 10.12086/oee.2021.210288

Citation:

Zhou X, Li X Y. Wavefront distortion prediction method based on motion estimation[J]. Opto-Electron Eng, 2021, 48(10): 210288. doi: 10.12086/oee.2021.210288

Wavefront distortion prediction method based on motion estimation

Zhou Xin^1,2,,
Li Xinyang^1, ,

1.
Key Laboratory of Adaptive Optics, Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu, Sichuan 610209, China
2.
University of Chinese Academy of Sciences, Beijing 100049, China

Fund Project: National Natural Science Foundation of China (62005286)

More Information

Corresponding author: Li Xinyang, E-mail: xyli@ioe.ac.cn

Received Date 06 September 2021

Revised Date 26 October 2021

Published Date 15 October 2021

Abstract

Abstract

In the actual adaptive optics control system, the time delay causes the mismatch between the correction profile generated by the corrector and the actual wavefront distortion, which leads to correction lag error. Under the atmospheric frozen flow turbulence assumption, a wavefront distortion prediction method based on motion estimation is proposed to compensate for the time delay. The template matching algorithm is used to estimate the atmospheric turbulence motion direction, according to the wavefront restored images of the reference frame and the current frame, and then the current frame is moved to predict the next frame. The prediction method applicability is evaluated, and the influence of backtracking frames on the prediction effect is discussed by comparing the simulation data of different sampling frequencies and different transverse wind speeds. The residual error is calculated with the template matching algorithm and the least recursive squares (RLS) algorithm. The simulation results show that the method performs better when the variation tendency of wavefront restored images is obvious. Therefore, the prediction effect can be maintained better in severe conditions. Finally, the prediction method is verified by using the actual observation data of Sirius, and the algorithm still keeps the prediction effect.
- adaptive optics /
- turbulence prediction /
- motion estimation /
- wavefront correction

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References

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Overview

Overview

Overview: In the actual adaptive optics control system, the time delay causes the mismatch between the correction profile generated by the corrector and the actual wavefront distortion, which leads to correction lag error. The longer the time delay, the worse the overall system control performance. Under the atmospheric frozen flow turbulence assumption, the atmospheric turbulence spatial characteristics will not change significantly in a certain time scale, and the atmospheric frozen flow turbulence is driven by the atmospheric transverse wind. According to the turbulence temporal aberration characteristics, a wavefront distortion prediction method based on motion estimation is proposed. Through the wavefront restored images of the reference frame and the current frame, the template matching algorithm can estimate the atmospheric turbulence motion direction, and then the current frame is moved to predict the next frame. Under the simulation conditions of the sampling frequency of 500 Hz, the wavelength of 550 nm, the telescope aperture of 1.8 m, and the phase screen numbers of 10, the overall ideal correction error of the 65 orders Zernike wavefront image can be reduced from 0.0614λ to 0.0508λ by predictive compensation, and the relative correction residual is 7.62%. Furthermore, the residual error is calculated with the template matching algorithm and the least recursive squares (RLS) algorithm to evaluate the prediction effect. By comparing different sampling frequencies and different transverse wind speeds, the method performs better when the variation tendency of wavefront restored images is obvious. Therefore, the prediction effect can be maintained better in severe conditions. Since the actual wavefront distortion deviates from the frozen flow turbulence assumption, two improved methods are proposed. The first one calculates the ideal prediction correction residuals, and the second one directly predicts the ideal correction residuals, which can further reduce the overall ideal correction error to 0.0343λ and 0.0242λ. Correspondingly, the prediction method is verified by using the actual observation data of Sirius, Hartmann sensor microlens array numbers of 156, the sub-aperture resolution of 16×16, the sampling frequency of 500 Hz, and backtracking frame numbers of 3. The overall ideal correction error promotion effects of the 65 orders Zernike wavefront image are 15.97% and 24.85% using two improved methods. Increasing the recovery area can slightly improve the prediction effect, but it is not proportional to the calculation cost. The experimental results fit the theoretical analysis well, which suggests that the algorithm has certain practical value and is helpful in actual adaptive optics control systems.