Deep learning for position fixing in the micron scale by using convolutional neural networks

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.