Calculation of spot entroid based on physical informed neural networks

Abstract

To determine the centroid of far-field laser beam spot with high precision and accuracy under intense noise contamination, a positioning algorithm named centroid-PINN is proposed, which is based on physical information neural network. A U-Net neural network is utilized to optimize the centroid estimation error. In order to demonstrate this new method, Gaussian spots polluted by two kinds of noises, i.e. ramp noise and white noise, are generated by simulation to train the neural network. The neural network is tested by two kinds of spots, i.e. Gaussian spot and Sinc-like spot. Both are predicted with high accuracy. Compared with traditional centroid method, the centroid-PINN needs no parameter tuning, especially can cope with ramp noise interference with high accuracy. This work will be conducive to developing the far-field laser beam spot measurement device, and can also serve as a reference for developing the Shack-Hartmann wavefront sensor.