Chinese Optics Letters, 2020, 18 (5): 050602, Published Online: Apr. 28, 2020   

Deep learning for position fixing in the micron scale by using convolutional neural networks Download: 797次

Author Affiliations
1 College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha 410073, China
2 State Key Laboratory of Pulsed Power Laser Technology, Changsha 410073, China
3 Hunan Provincial Key Laboratory of High Energy Laser Technology, Changsha 410073, China
4 Tianjin Navigation Instruments Research Institute, Tianjin 300131, China
Abstract
We propose here a novel method for position fixing in the micron scale by combining the convolutional neural network (CNN) architecture and speckle patterns generated in a multimode fiber. By varying the splice offset between a single mode fiber and a multimode fiber, speckles with different patterns can be generated at the output of the multimode fiber. The CNN is utilized to learn these specklegrams and then predict the offset coordinate. Simulation results show that predicted positions with the precision of 2 μm account for 98.55%. This work provides a potential high-precision two-dimensional positioning method.

Hongye Li, Hu Liang, Qihao Hu, Meng Wang, Zefeng Wang. Deep learning for position fixing in the micron scale by using convolutional neural networks[J]. Chinese Optics Letters, 2020, 18(5): 050602.

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