In this paper, research was conducted on Deep Learning Wavefront Sensing (DLWS) neural networks using simulated atmospheric turbulence datasets, and a novel DLWS was proposed based on attention mechanisms and Convolutional Neural Networks (CNNs). The study encompassed both indoor experiments and kilometer-range laser transmission experiments employing DLWS. In terms of indoor experiments, data were collected and training was performed on the platform built by us. Subsequent comparative experiments with the Shack-Hartmann Wavefront Sensing (SHWS) method revealed that our DLWS model achieved accuracy on par with SHWS. For the kilometer-scale experiments, we directly applied the DLWS model obtained from the indoor platform, eliminating the need for new data collection or additional training. The DLWS predicts the wavefront from the beacon light PSF in real time and then uses it for aberration correction of the emitted laser. The results demonstrate a substantial improvement in the average peak intensity of the light spot at the target position after closed-loop correction, with a remarkable increase of 5.35 times compared to the open-loop configuration.

This study addresses the modeling and simulation challenges of urban laser countermeasure systems against Low-Slow-Small (LSS) UAVs by proposing a physics simulation framework integrating Geographic Information System (GIS)-based dynamic 3D real-world scenes and constructing a hybrid Anti-UAV dataset combining real and simulated data. A three-stage target tracking system is developed, encompassing target acquisition, coarse tracking, and precise tracking. Furthermore, the UAV-D-Fine detection algorithm is introduced, significantly improving small-target detection accuracy and efficiency. The simulation platform achieves dynamic fusion between target models and GIS real-scene models, enabling a full physical simulation of UAV takeoff, tracking, aiming, and laser engagement, with additional validation of laser antenna tracking performance. Experimental results demonstrate the superior performance of the proposed algorithm in both simulated and real-world environments, ensuring accurate UAV detection and sustained tracking, thereby providing robust support for low-altitude UAV laser countermeasure missions.

Wavefront sensing is an essential technique in optical imaging, adaptive optics, and atmospheric turbulence correction. Traditional wavefront reconstruction methods, including the Gerchberg–Saxton (GS) algorithm and phase diversity (PD) techniques, are often limited by issues such as low inversion accuracy, slow convergence, and the presence of multiple possible solutions. Recent developments in deep learning have led to new methods, although conventional CNN-based models still face challenges in effectively capturing global context. To overcome these limitations, we propose a Transformer-based single-shot wavefront sensing method, which directly reconstructs wavefront aberrations from focal plane intensity images. Our model integrates a Normalization-based Attention Module (NAM) into the CoAtNet architecture, which strengthens feature extraction and leads to more accurate wavefront characterization. Experimental results in both simulated and real-world conditions indicate that our method achieves a 4.5% reduction in normalized wavefront error (NWE) compared to ResNet34, suggesting improved performance over conventional deep learning models. Additionally, by leveraging Walsh function modulation, our approach resolves the multiple-solution problem inherent in phase retrieval techniques. The proposed model achieves high accuracy, fast convergence, and simplicity in implementation, making it a promising solution for wavefront sensing applications.