论文标题
迈向基于人造智能的光学闪烁计：缩放问题
Towards an Artificial-Intelligence-Based Optical Scintillometer: Scaling Issue
论文作者
论文摘要
考虑了基于深度神经网络（DNN）的强度闪烁模式的处理，大气湍流强度（CN2参数）感应。结果表明，通过使用对名义距离L0训练的DNN获得的CN2值的缩放，可以避免使用传播距离变化的DNN重新训练。可以使用源自Kolmogorov湍流理论（基于理论的缩放）或通过波播数值建模和模拟（基于M＆S的缩放）得出的分析表达来获得所需的CN2缩放系数。
Atmospheric turbulence strength (Cn2 parameter) sensing based on processing of intensity scintillation patterns with deep neural network (DNN) is considered. It is shown that DNN re-training with propagation distance change can be avoided by scaling of Cn2 values obtained using a DNN trained for a nominal distance L0 . The required Cn2 scaling factor can be obtained using either an analytical expression derived from the Kolmogorov turbulence theory (theory-based scaling), or through wave-optics numerical modeling and simulations (M&S-based scaling).
