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James Leger, Editor-in-Chief
Liying Tan, Yubin Cao, Jing Ma, and Kangning Li
Liying Tan,1 Yubin Cao,1,* Jing Ma,1 and Kangning Li2
1Free-space Optical Communication Technology Research Center, Harbin Institute of Technology, Harbin 150001, China
2China Academy of Electronics and Information Technology, Beijing 100041, China
*Corresponding author: email@example.com
Optical image tracing is one of key technologies to realize and maintain satellite-to-ground laser communication. Since machine learning has been proved to be a powerful tool for modeling nonlinear system, a model containing a preprocessing module, a CNN module (Convolutional Neural Network Module) as well as a LSTM module (Long-Short Term Neural Network Memory Module) was developed to process digital images in time series and then predict centroid positions under the influence of atmospheric turbulence. Different from most previous models composed of neural networks, some important physical situations are considered for light fields distributed on CMOS. By building and training this model, centroid positions can be predicted in real time for practical applications in laser satellite communication.
© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
Qi Xin, Guohao Ju, Chunyue Zhang, and Shuyan Xu
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