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Distortion wavefront prediction method based on deep learning

A distorted wavefront and deep learning technology, applied in the field of wavefront prediction and correction, and distorted wavefront prediction, can solve the problems of distorting mirror compensation wavefront lag, etc., to avoid the effect of correction errors

Pending Publication Date: 2022-05-20
INST OF OPTICS & ELECTRONICS - CHINESE ACAD OF SCI
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Problems solved by technology

[0006] The technical problem solved by the present invention is: to overcome the deformable mirror caused by the inherent time delay error of the AO system when correcting the distortion wavefront of atmospheric turbulence with high time frequency The problem that the compensation wavefront on the above obviously lags behind the change of the distortion wavefront

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  • Distortion wavefront prediction method based on deep learning
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[0020] In order to make the technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with specific implementation examples and with reference to the accompanying drawings.

[0021] figure 1 It is a workflow flowchart of a wavefront prediction method based on deep learning, and the specific steps are:

[0022] Step S1: Atmospheric turbulence is modeled according to the theory of atmospheric freezing, which assumes a time scale of 10-20ms, and in a few cases reaches 50-100ms. The sampling frequency of the AO system is generally about 1000 Hz, and the time delay is 2-3 sampling periods. Therefore, within a time delay of 2-3ms, the assumption of atmospheric freezing turbulence is reasonable, so we use the phase covariance function to simulate in MATLAB to generate a dynamic phase screen that conforms to the statistical distribution of atmospheric turbulence, and obtain the actual distorted ...

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Abstract

The invention discloses a distortion wavefront prediction method based on deep learning. The adaptive optical system (AO) is a servo delay system capable of compensating atmospheric turbulence distortion wavefront in real time, the delay time is generally 2-3 sampling periods, the wavefront prediction technology can effectively solve the time delay problem, and the control performance of the system is improved. Deep learning can be used for self-extracting image deep features and can be used for fitting mapping from multi-frame historical wavefront information to predicted wavefront information; according to the method, the nonlinear fitting capability of a neural network is fully exerted, the redundant information between two adjacent frames is eliminated in a residual learning mode by utilizing the time sequence characteristics existing between the frames of the multi-frame continuous distortion wavefront, then the refined characteristics are fused, and finally, the nonlinear fitting capability of the neural network is fully utilized. And a final prediction result is obtained by fusing the features of all levels and is used for real-time correction, so that the time delay error of the AO system when the AO system deals with the atmospheric turbulence distortion wavefront with high time frequency is reduced, and the control performance is improved.

Description

technical field [0001] The invention belongs to the technical field of wavefront prediction control, and relates to a distortion wavefront prediction method based on deep learning, which is suitable for wavefront prediction and correction in an adaptive optics system. Background technique [0002] In the case of correcting the distortion wavefront of atmospheric turbulence with high time frequency, the AO system usually has a delay of 2-3 sampling periods due to the delay in reading data from the wavefront sensor and the delay in control calculation, which will cause compensation on the deformable mirror The wavefront lags significantly behind changes in the distorted wavefront, severely limiting correction performance. The traditional proportional-integral (PI) control technology commonly used in AO systems at present cannot fundamentally solve the delay problem, so when dealing with high time-frequency wavefront aberrations, the correction effect is not good. For this rea...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/27G06F119/02
CPCG06F30/27G06F2119/02Y02A90/10
Inventor 朱里程王宁马帅葛欣兰杨平
Owner INST OF OPTICS & ELECTRONICS - CHINESE ACAD OF SCI
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