Atmospheric turbulence degrades the performance of free-space optical(FSO)communication and remote sensing systems by introducing phase and intensity distortions.While a majority of research focuses on mitigating thes...Atmospheric turbulence degrades the performance of free-space optical(FSO)communication and remote sensing systems by introducing phase and intensity distortions.While a majority of research focuses on mitigating these effects to ensure robust signal transmission,an underexplored alternative is to leverage the transformation of structured light to characterize the turbulent medium itself.Here,we introduce a deep learning framework that fuses post-propagation intensity speckle patterns and orbital angular momentum(OAM)spectral data for atmospheric turbulence parameter inference.Our architecture,based on a modified InceptionNet backbone,is optimized to extract and integrate multi-scale features from these distinct optical modalities.This multimodal approach achieves validation accuracies exceeding 80%,substantially outperforming conventional single-modality baselines.The framework demonstrates high inference accuracy and enhanced training stability across a broad range of simulated turbulent conditions,quantified by varying Fried parameters(r_(0))and Reynolds numbers(Re).This work presents a scalable and data-efficient method for turbulence characterization,offering a pathway toward robust environmental sensing and the optimization of dynamic FSO systems.展开更多
基金111 Project(B17035)National Natural Science Foundation of China(U20B2059,62575227,62231021,61621005,62201613)+1 种基金Shanghai Aerospace Science and Technology Innovation Foundation(SAST-2022-069)Fundamental Research Funds for the Central Universities(ZYTS25121).
文摘Atmospheric turbulence degrades the performance of free-space optical(FSO)communication and remote sensing systems by introducing phase and intensity distortions.While a majority of research focuses on mitigating these effects to ensure robust signal transmission,an underexplored alternative is to leverage the transformation of structured light to characterize the turbulent medium itself.Here,we introduce a deep learning framework that fuses post-propagation intensity speckle patterns and orbital angular momentum(OAM)spectral data for atmospheric turbulence parameter inference.Our architecture,based on a modified InceptionNet backbone,is optimized to extract and integrate multi-scale features from these distinct optical modalities.This multimodal approach achieves validation accuracies exceeding 80%,substantially outperforming conventional single-modality baselines.The framework demonstrates high inference accuracy and enhanced training stability across a broad range of simulated turbulent conditions,quantified by varying Fried parameters(r_(0))and Reynolds numbers(Re).This work presents a scalable and data-efficient method for turbulence characterization,offering a pathway toward robust environmental sensing and the optimization of dynamic FSO systems.
