蒙特卡罗方法
物理
散射
光学
方位角
涡流
计算物理学
传输(电信)
前向散射
角谱法
梁(结构)
云计算
角动量
米氏散射
跟踪(教育)
旋涡
领域(数学)
干扰(通信)
视野
能见度
相(物质)
极化(电化学)
近场和远场
计算机模拟
作者
Chengshuai Cui,Zijing Zhang,Hongyang Wang,Xinran Lv,Jiayu Song
标识
DOI:10.1088/1612-202x/ae76fc
摘要
Abstract Vortex beams carry unique orbital angular momentum (OAM) and show great potential in target recognition and azimuth measurement. However, in practical scenarios, scattering media such as cloud and fog disturb their intensity, phase, and OAM spectra, thereby limiting their applications in complex environments. To address the difficulty of traditional Monte Carlo (MC) methods in obtaining optical field information and the low computational efficiency of the particle-screen method, this study proposes a vortex beam transmission model in cloud and fog based on the Fast electric-field Monte Carlo (FEMC) method. By incorporating optical field tracking into the traditional MC framework and introducing a preconstructed particle parameter pool for rapid parameter retrieval, the proposed method improves simulation efficiency. Based on this method, the evolution of the intensity, phase, and OAM spectra of vortex beams under different transmission distances, visibilities, topological charges and beam waist radii are investigated, and quantitative evaluations are conducted using the peak signal-to-noise ratio ( PSNR ) and OAM spectrum purity. The results show that, as the transmission distance increases, intensity diffusion, phase distortion, and OAM spectral crosstalk become more severe. As visibility increases, both PSNR and OAM spectrum purity improve significantly, indicating that the spatial structure and modal characteristics can be preserved more effectively under weaker scattering conditions. Under strong cloud and fog scattering conditions, the OAM spectrum distribution of lower-order vortex beams can be better preserved. The proposed FEMC method provides an effective tool for characterizing the optical field evolution and OAM spectral variations of vortex beams in scattering media, offering theoretical and simulation support for related studies and applications in complex environments.
科研通智能强力驱动
Strongly Powered by AbleSci AI