Deep-learning-based phase control method for tiled aperture coherent beam combining systems

查看全文 作　　者：Tianyue [1]Hou;Yi [1]An;Qi [1]Chang;Pengfei [1]Ma;Jun [1]Li;Dong [2]Zhi;Liangjin [1]Huang;Rongtao [1]Su;Jian [1]Wu;Yanxing [1]Ma;Pu [1]Zhou

机构地区：[1]College of Advanced Interdisciplinary Studies,National University of Defense Technology,Changsha 410073,China;[2]Hypervelocity Aerodynamics Institute,China Aerodynamics Research and Development Center,Mianyang 621000,China

出　　处：《High Power Laser Science and Engineering》索引2019年第7卷第4期,共7页

基　　金：supported by the National Natural Science Foundation of China(Nos.61705264 and 61705265);the Natural Science Foundation of Hunan Province,China(No.2019JJ10005). 摘　　要：We incorporate deep learning(DL)into tiled aperture coherent beam combining(CBC)systems for the first time,to the best of our knowledge.By using a well-trained convolutional neural network DL model,which has been constructed at a non-focal-plane to avoid the data collision problem,the relative phase of each beamlet could be accurately estimated,and then the phase error in the CBC system could be compensated directly by a servo phase control system.The feasibility and extensibility of the phase control method have been demonstrated by simulating the coherent combining of different hexagonal arrays.This DL-based phase control method offers a new way of eliminating dynamic phase noise in tiled aperture CBC systems,and it could provide a valuable reference on alleviating the long-standing problem that the phase control bandwidth decreases as the number of array elements increases. 关键词：coherent beam combining deep learning phase control

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Deep-learning-based phase control method for tiled aperture coherent beam combining systems

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