Investigations of incoherent beam combining using stochastic parallel gradient descent with retroreflector target

被引:1
|
作者
Henriksson, Markus [1 ]
Brannlund, Carl [1 ]
机构
[1] FOI Swedish Def Res Agcy, Olaus Magnus Vag 42, S-58330 Linkoping, Sweden
关键词
Incoherent beam combining; stochastic parallel gradient descent; retro-reflection; laser dazzling;
D O I
10.1117/12.2533022
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
Incoherent beam combining of laser beams can increase the available power on target when the output power of a single laser source is limited, and it may also reduce turbulence effects by averaging of scintillations. We have investigated the optimization performance of different variations of the stochastic parallel gradient descent (SPGD) algorithm in a setup where five low power laser beams illuminate a cat's eye retroreflector, and detectors next to the lasers are used to provide feedback for optimization. Angular adjustments of the laser beams are provided by displacement of fiber tips behind collimating lenses. This setup is representative of a dazzling application. Findings include that the optimization demands that there is some initial signal from all laser beams to provide rapid and dependable optimization, which means that the initial pointing errors cannot be much larger than the divergence of the individual beams. Parameter variations show that the sensitivity to settings is relatively low, often a factor two interval of parameter values give an acceptable performance.
引用
收藏
页数:8
相关论文
共 50 条
  • [41] Test of the stochastic parallel gradient descent algorithm in laboratory experiments
    Banakh V.A.
    Larichev A.V.
    Razenkov I.A.
    Shesternin A.N.
    [J]. Atmospheric and Oceanic Optics, 2013, 26 (4) : 337 - 344
  • [42] Wavefront error correction with stochastic parallel gradient descent algorithm
    Liu Jiaguo
    Li Lin
    Hu Xinqi
    Yu Xin
    Zhao Lei
    [J]. OPTICAL DESIGN AND TESTING III, PTS 1 AND 2, 2008, 6834
  • [43] Weighted Aggregating Stochastic Gradient Descent for Parallel Deep Learning
    Guo, Pengzhan
    Ye, Zeyang
    Xiao, Keli
    Zhu, Wei
    [J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2022, 34 (10) : 5037 - 5050
  • [44] High Performance Parallel Stochastic Gradient Descent in Shared Memory
    Sallinen, Scott
    Satish, Nadathur
    Smelyanskiy, Mikhail
    Sury, Samantika S.
    Re, Christopher
    [J]. 2016 IEEE 30TH INTERNATIONAL PARALLEL AND DISTRIBUTED PROCESSING SYMPOSIUM (IPDPS 2016), 2016, : 873 - 882
  • [46] Illumination by using incoherent beam combining technology
    Sun, Quan
    Liu, Zhiwei
    He, Yulong
    Ning, Yu
    Xu, Xiaojun
    Xi, Fengjie
    [J]. OPTIK, 2022, 251
  • [47] Coherent beam combination of two-dimensional high power fiber amplifier array using stochastic parallel gradient descent algorithm
    Zhou, Pu
    Liu, Zejin
    Wang, Xiaolin
    Ma, Yanxing
    Ma, Haotong
    Xu, Xiaojun
    [J]. APPLIED PHYSICS LETTERS, 2009, 94 (23)
  • [48] Near-diffraction-limited flattop laser beam adaptively generated by stochastic parallel gradient descent algorithm
    Ma, Haotong
    Liu, Zejin
    Xu, Xiaojun
    Wang, Sanhong
    Liu, Changhai
    [J]. OPTICS LETTERS, 2010, 35 (17) : 2973 - 2975
  • [49] Beam phase-distortion correction in a high power laser based on the stochastic parallel gradient descent technique
    Wang, D. E.
    Hu, D. X.
    Zhou, W.
    Deng, X. W.
    Wang, Y. C.
    Deng, W.
    Yuan, Q.
    Zhang, X.
    Huang, L.
    Li, T. H.
    Xue, Q.
    Yuan, H. Y.
    Peng, Z. T.
    Dai, W. J.
    Zhu, Q. H.
    Jing, F.
    [J]. LASER PHYSICS LETTERS, 2013, 10 (12)
  • [50] Stochastic parallel gradient descent laser beam control algorithm for atmospheric compensation in free space optical communication
    Cao, Jingtai
    Zhao, Xiaohui
    Li, Zhaokun
    Liu, Wei
    Song, Yang
    [J]. OPTIK, 2014, 125 (20): : 6142 - 6147