PredictionNet: a long short-term memory-based attention network for atmospheric turbulence prediction in adaptive optics

Abstract

Adaptive optics (AO) has great applications in many fields and has attracted wide attention from researchers. However, both traditional and deep learning-based AO methods have inherent time delay caused by wavefront sensors and controllers, leading to the inability to truly achieve real-time atmospheric turbulence correction. Hence, future turbulent wavefront prediction plays a particularly important role in AO. Facing the challenge of accurately predicting stochastic turbulence, we combine the convolutional neural network with a turbulence correction time series model and propose a long short-term memory attention-based network, named PredictionNet, to achieve real-time AO correction. Especially, PredictionNet takes the spatiotemporal coupling characteristics of turbulence wavefront into consideration and can improve the accuracy of prediction effectively. The combination of the numerical simulation by a professional software package and the real turbulence experiment by digital holography demonstrates in detail that PredictionNet is more accurate and more stable than traditional methods. Furthermore, the result compared with AO without prediction confirms that predictive AO with PredictionNet is useful.