Open AccessOptical image centroid prediction based on machine learning for laser satellite communication
Author(s) -
Liying Tan,
Yubin Cao,
Jing Ma,
Kangning Li
Publication year - 2019
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.27.026615
Subject(s) - computer science , centroid , convolutional neural network , artificial neural network , preprocessor , artificial intelligence , free space optical communication , image processing , computer vision , optical communication , electronic engineering , image (mathematics) , engineering
Optical image tracing is one of key technologies to realize and maintain satellite-to-ground laser communication. Since machine learning has been proved to be a powerful tool for modeling nonlinear system, a model containing a preprocessing module, a CNN module (Convolutional Neural Network Module) as well as a LSTM module (Long-Short Term Neural Network Memory Module) was developed to process digital images in time series and then predict centroid positions under the influence of atmospheric turbulence. Different from most previous models composed of neural networks, some important physical situations are considered for light fields distributed on CMOS. By building and training this model, centroid positions can be predicted in real time for practical applications in laser satellite communication.