深度学习技术在空间激光通信中的应用  

Application of deep learning technology in free space laser communication

作　　者：黄永梅[1,2,3] 李宏伟 贺东 王强[1,2,3] 郭弘扬 王子豪[1,2,3] 唐薇 HUANG Yongmei;LI Hongwei;HE Dong;WANG Qiang;GUO Hongyang;WANG Zihao;TANG Wei(Key Laboratory of Optical Engineering,Chinese Academy of Sciences,Chengdu 610209,China;Institute of Optics and Electronics,Chinese Academy of Sciences,Chengdu 610209,China;University of Chinese Academy of Sciences,Beijing 100049,China;Nantong University Research Center for Intelligent Information Technology,Nantong 226019,China)

机构地区：[1]中国科学院光束控制重点实验室,成都610209 [2]中国科学院光电技术研究所,成都610209 [3]中国科学院大学,北京100049 [4]南通大学智能信息技术研究中心,南通226019

出　　处：《遥测遥控》2022年第4期89-96,共8页Journal of Telemetry,Tracking and Command

摘　　要：与射频通信相比,空间激光通信具有传输速率高、保密性能强、终端功耗低等优点,目前已成为当前通信领域的一个研究热点。同时,空间激光通信也面临着一些严峻的技术挑战,如大气湍流导致空间激光通信的信道情况十分复杂,复杂的信道会引发信号光强度起伏剧烈,信标光跟踪与瞄准困难,接收端的信号光场波前畸变严重等。为了提升空间激光通信在复杂信道环境中的性能,学者们将深度学习技术引入到空间激光通信系统中。多项研究表明,深度学习在空间激光通信的诸多方面表现出了优越的信息处理能力。对近年来深度学习技术在空间激光通信信号处理与检测,信标光捕获与跟踪以及波前畸变探测与校正等方面的应用做一全面梳理,并对用于空间激光通信的深度学习技术的前景进行展望。To compare with radio communication,free space laser communication is owning to higher data rate,higher security and less power dissipation,has become one of the research hotspot.However,the performance of free space laser communication is limited to some factors,such as signal fluctuation,the difficulties of tracking and pointing mechanism,and the wavefront aberration caused by atmosphere turbulence.In order to improve the performance in complex environment,the deep learning method is introduced to free space laser communication.Various studies have shown that the deep learning method can process signal with obvious advantages in many aspects of free space laser communication.This paper makes a review of the deep learning method used for free space laser communication signal processing and detection,the tracking and pointing mechanism,and wavefront aberration detection and correction.Finally,the prospect of deep learning technology for FSOC is prospected.

关 键 词：空间激光通信 深度学习 信号处理 光斑 波前畸变 

分 类 号：V443.1[航空宇航科学与技术—飞行器设计]