Performance of a U-Net-based neural network for predictive adaptive optics

Performance of the neural network-based prediction model in closed-loop adaptive optics

Adaptive optics (AO) technology is an effective means to compensate for atmospheric turbulence, but the inherent delay error of an AO system will cause the compensation phase of the deformable mirror (DM) to lag behind the actual distortion, which limits the correction performance of the AO technology. Therefore, the feed-forward prediction of atmospheric turbulence has important research value and application significance to offset the inherent time delay and improve the correction bandwidth of the AO system. However, most prediction algorithms are limited to an open-loop system, and the deployment and the application in the actual AO system are rarely reported, so its correction performance improvement has not been verified in practice. We report, to our knowledge, the first successful test of a deep learning-based spatiotemporal prediction model in an actual 3 km laser atmospheric transport AO system and compare it with the traditional closed-loop control methods, demonstrating that the AO system with the prediction model has higher correction performance.

Highly Stable Spatio-Temporal Prediction Network of Wavefront Sensor Slopes in Adaptive Optics

Adaptive Optics (AO) technology is an effective means to compensate for wavefront distortion, but its inherent delay error will cause the compensation wavefront on the deformable mirror (DM) to lag behind the changes in the distorted wavefront. Especially when the change in the wavefront is higher than the Shack-Hartmann wavefront sensor (SHWS) sampling frequency, the multi-frame delay will seriously limit its correction performance. In this paper, a highly stable AO prediction network based on deep learning is proposed, which only uses 10 frames of prior wavefront information to obtain high-stability and high-precision open-loop predicted slopes for the next six frames. The simulation results under various distortion intensities show that the prediction accuracy of six frames decreases by no more than 15%, and the experimental results also verify that the open-loop correction accuracy of our proposed method under the sampling frequency of 500 Hz is better than that of the traditional non-predicted method under 1000 Hz.

A nested U-shaped network for accurately predicting directional scattering of all-dielectric nanostructures

Forward prediction of directional scattering from all-dielectric nanostructures by a two-level nested U-shaped convolutional neural network (U²-Net) is investigated. Compared with the traditional U-Net method, the U²-Net model with lower model height outperforms for the case of a smaller image size. For the input image size of 40 × 40, the prediction performance of the U²-Net model with the height of three is enhanced by almost an order of magnitude, which can be attributed to the more excellent capacity in extracting richer multi-scale features. Since it is the common problem in nanophotonics that the model height is limited by the smaller image size, our findings can promote the nested U-shaped network as a powerful tool applied to various tasks concerning nanostructures.

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

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.

A Neural Network-Based Method for Fast Capture and Tracking of Laser Links between Nonorbiting Platforms

In this paper, a neural network approach is used to conduct an in-depth study and analysis of the fast capture tracking method for laser links between nonorbiting platforms. The experimental platform of the convolutional neural network- (CNN-) based free-space optical communication (FSO) wavefront correction system is built indoors, and the wavefront distortion correction performance of the CNN-based wavefront correction method is investigated. The experimental results show that the coupling power loss can be reduced to small after the CNN method correction under weak and strong turbulence. The accuracy of the above model is verified by comparing the simulation data with the experimentally measured data, thus realizing the coordinate decoupling of the coarse aiming mechanism and weakening the influence of structural factors on the tracking accuracy of the system. The tracking correlation equation of the influence of beam far-field dynamic characteristics on the tracking stability of the link is established, and the correlation factor variance of beam far-field dynamic characteristics is used to provide a quantitative analysis method for the evaluation and prediction of the comprehensive performance of the link tracking stability. The influence of beam divergence angle, wavefront distortion, detector accuracy, and atmospheric turbulence disturbance on the correlation factor variance of beam far-field dynamic characteristics of laser link beacons is modelled, and the link tracking stability optimization method is proposed under the requirement of link tracking accuracy, which provides an effective solution analysis method to realize the improvement of laser link tracking stability.