Scholarlink

<p>Precise characterization of atmospheric turbulence is fundamental to free-space optical communications and imaging. While vortex beams are effective turbulence probes, existing sensing methods rely on a spatially integrated one-dimensional (1D) orbital angular momentum (OAM) spectrum, which obscures the heterogeneous nature of atmospheric distortions. Via numerical simulations, here we introduce radially resolved two-dimensional (2D) OAM spectroscopy, <i>P</i>(<i>m</i>, <i>n</i>), which resolves the azimuthal OAM spectrum (topological charge <i>m</i>) across discrete radial annuli (index <i>n</i>). This framework captures the spatially dependent beam-turbulence interactions of complex structured probes, such as multi-ringed Bessel–Gaussian beams. Coupled with a Support Vector Machine classifier, this method achieves a median classification accuracy of 86% across 25 distinct turbulence conditions, a 23% improvement over conventional 1D techniques. Additionally, we establish a targeted feature-selection protocol to mitigate peripheral noise. Our proposed technique to co-design the probe field is of immediate use in optical sensing as it can enable high-fidelity environmental characterization.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Spatially-resolved atmospheric turbulence sensing with two-dimensional orbital angular momentum spectroscopy

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月19日

查看全文
  • Wenjie Jiang,
  • Mingjian Cheng,
  • Lixin Guo,
  • Andrew Forbes

摘要

Precise characterization of atmospheric turbulence is fundamental to free-space optical communications and imaging. While vortex beams are effective turbulence probes, existing sensing methods rely on a spatially integrated one-dimensional (1D) orbital angular momentum (OAM) spectrum, which obscures the heterogeneous nature of atmospheric distortions. Via numerical simulations, here we introduce radially resolved two-dimensional (2D) OAM spectroscopy, P(m, n), which resolves the azimuthal OAM spectrum (topological charge m) across discrete radial annuli (index n). This framework captures the spatially dependent beam-turbulence interactions of complex structured probes, such as multi-ringed Bessel–Gaussian beams. Coupled with a Support Vector Machine classifier, this method achieves a median classification accuracy of 86% across 25 distinct turbulence conditions, a 23% improvement over conventional 1D techniques. Additionally, we establish a targeted feature-selection protocol to mitigate peripheral noise. Our proposed technique to co-design the probe field is of immediate use in optical sensing as it can enable high-fidelity environmental characterization.