从数组波导来解码连贯模式,用于自由空间光学到达角度估计
Jinwen Zhang1, Haitao Zhang1, Zhuoyi Yang1
1State Key Laboratory of Precision Space-Time Information Sensing Technology, Department of Precision Instrument, Tsinghua University, Beijing 100084, China.
Sensors (Basel, Switzerland)
|December 11, 2025
概括
本研究引入了一种使用波导解码和深度学习的新光学到达角度 (AOA) 估计方法. 该技术实现了高精度和稳定性,克服了传统AOA传感器的局限性.
科学领域:
- 光子学 是一个光子学.
- 光学传感传感器是什么?
- 人工智能的人工智能
背景情况:
- 传统的到达角度 (AOA) 估计方法在小型化,复杂性和可靠性方面面临挑战.
- 现有的技术很难满足对紧和强大的光学传感器的需求.
研究的目的:
- 提出一种新的自由空间光学AOA估计方法,克服传统技术的局限性.
- 为了利用阵列波导连贯模式解码和深度学习来精确的空间角度信息检索.
主要方法:
- 利用与AOA相关的相差,从发生光传播和干扰到阵列输入波导中.
- 在板波导输出处形成多束干扰边缘,由阵列输出波导取样.
- 雇佣了一个经过培训的卷积神经网络 (CNN) -注意力回归器用于AOA估计.
主要成果:
- 在40度视野中,达到0.0142°的平均绝对误差 (MAE) 和0.0193°的根平均平方误差 (RMSE).
- 与传统的峰值线性校准和其他神经网络架构相比,证明了更高的精度.
- 对模拟相位噪声和制造公差表现出了显著的稳定性.
结论:
- 这种新方法有效地解码光学信号的空间角度信息.
- 突出了集成光子学和深度学习之间的协同作用,用于先进的光学传感.
- 为高度集成,强大和高性能芯片内光学传感器铺平了道路.
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