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Residual network-based aberration correction in a sensor-less adaptive optics system
W. Liu; X. Ma; D. Jin; W. Shi; H. Gu and J. Cao
2023
Source PublicationOptics Communications
ISSN00304018
Volume545
AbstractThe performance of free-space optical communication (FSOC) is often affected by atmospheric turbulence. The sensor-less adaptive optics (SLAO) system is an effective method for overcoming the effects of atmospheric turbulence. The performance of the control algorithm in the SLAO system directly determines whether the SLAO system can effectively correct wavefront aberrations. In this study, we introduce a residual network (ResNet) as a control algorithm to replace the traditional control algorithm. By lowering the number of iterations, this strategy enhances the real-time performance of the FSOC system. The final ResNet model can achieve an accuracy of 0.98 for training and 0.92 for testing. The simulation results show that stochastic parallel gradient descent (SPGD) algorithm takes 700 times longer and requires at least 500 iterations to achieve the same performance as ResNet. And we verify the feasibility of the ResNet model by setting up an experiment. © 2023 Elsevier B.V.
DOI10.1016/j.optcom.2023.129707
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Indexed Bysci ; ei
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Document Type期刊论文
Identifierhttp://ir.ciomp.ac.cn/handle/181722/67721
Collection中国科学院长春光学精密机械与物理研究所
Recommended Citation
GB/T 7714
W. Liu,X. Ma,D. Jin,et al. Residual network-based aberration correction in a sensor-less adaptive optics system[J]. Optics Communications,2023,545.
APA W. Liu,X. Ma,D. Jin,W. Shi,&H. Gu and J. Cao.(2023).Residual network-based aberration correction in a sensor-less adaptive optics system.Optics Communications,545.
MLA W. Liu,et al."Residual network-based aberration correction in a sensor-less adaptive optics system".Optics Communications 545(2023).
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