使用SKCSWT-Net识别高阶辐射拉盖尔-高斯束叠加状态的模式参数识别
Optics express
|July 30, 2025
概括
一个新的AI模型,SKCSWT-Net,通过大气流来有效地解码旋束中的模态参数. 它在识别拉盖尔-高斯波束索引方面达到很高的准确性,即使存在干扰.
科学领域:
- 光学物理学的光学物理.
- 人工智能的人工智能是人工智能.
- 大气光学是大气光学.
背景情况:
- 束携带复杂的信息,对光通信至关重要.
- 动荡的大气道扭曲这些光束,使数据检索复杂化.
- 对于强大的光学系统来说,模式参数的高效解复是必不可少的.
研究的目的:
- 提出一种新型的人工智能模型,用于在叠加的束中高效地解复模态参数.
- 为了提高通过大气流传输的拉盖尔-高斯光束 (LGBs) 的亚齐图斯和辐射指数的识别精度.
- 在各种条件下评估模型的性能和稳定性.
主要方法:
- 开发SKCSWT-Net,集成选择性内核 (SK) 和交叉窗口变压器 (CSWT) 模块.
- 训练和测试模型的叠加LGBs与不同的模式参数.
- 在长距离的模拟大气流道中进行实验验证.
- 对阻塞和像素损失进行稳定性测试.
主要成果:
- 在1000米以上的16种叠加的LGB中,SKCSWT-Net实现了99.03%的识别准确度.
- 在2000米和850nm波长下分别记录了89%和97%的精度.
- 该模型在屏蔽和像素损失下保持了超过84.91%的准确性,显示出显著的稳定性.
结论:
- 在乱的大气通道中,SKCSWT-Net提供了一种高效准确的解决方案,用于解复杂化模式参数.
- 该模型的高性能和稳定性对先进的光通信系统具有前景.
- 这种人工智能驱动的方法显著推进了束信息检索领域.
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