High Power Laser Science and Engineering, Vol. 7, Issue 4, 04000e59 (2019)
Deep-learning-based phase control method for tiled aperture coherent beam combining systems
Tianyue Hou1, Yi An1, Qi Chang1, Pengfei Ma1,†, Jun Li1, Dong Zhi2, Liangjin Huang1, Rongtao Su1, Jian Wu1, Yanxing Ma1, and Pu Zhou1,†
- 1College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha 410073, China
- 2Hypervelocity Aerodynamics Institute, China Aerodynamics Research and Development Center, Mianyang 621000, China
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.