信像平移自混合干涉中可变光反馈耦合系数的估计。

Applied optics Pub Date : 2025-09-10 DOI:10.1364/AO.567935
Asra Abid Siddiqui, Wajahat Hussain, Olivier D Bernal, Usman Zabit
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引用次数: 0

摘要

准确估计光反馈耦合因子C对于基于激光的自混合干涉(SMI)传感器的可靠运行至关重要,特别是在可变反馈条件下。在这项工作中,我们提出了一种新的基于深度学习的方法,据我们所知,在强、中、弱反馈条件下,从SMI信号中估计时变C因子。所提出的方法利用将一维(1D) SMI信号转换为二维(2D)图像表示,允许卷积神经网络提取上下文丰富的特征,从而提高估计性能。与传统的图像处理任务不同,该方法将问题表述为信号到图像的转换,为传感器参数推断量身定制。实验结果表明,这种基于2d的方法优于最先进的循环神经网络模型,包括LSTM和变压器架构,特别是在对采样频率、位移幅度和反馈机制变化的鲁棒性方面。该方法是通用的,适用于广泛的传感器信号分析任务。为了支持可再现性和实际采用,我们提供了一个公开可用的方法实现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Estimation of variable optical feedback coupling factor for self-mixing interferometry by signal-to-image translation.

Accurate estimation of the optical feedback coupling factor C is essential for the reliable operation of laser-based self-mixing interferometry (SMI) sensors, particularly in variable feedback conditions. In this work, we present a novel deep learning-based method, to the best of our knowledge, to estimate the time-varying C factor from SMI signals under strong, moderate, and weak feedback conditions. The proposed approach leverages a transformation of one-dimensional (1D) SMI signals into two-dimensional (2D) image representations, allowing convolutional neural networks to extract context-rich features that enhance estimation performance. Unlike traditional image-processing tasks, this method formulates the problem as a signal-to-image translation, tailored for sensor parameter inference. Experimental results demonstrate that this 2D-based approach outperforms state-of-the-art recurrent neural network models, including LSTM and transformer architectures, particularly in terms of robustness to changes in sampling frequency, displacement amplitude, and feedback regime. The methodology is generic and applicable to a wide range of sensor signal analysis tasks. To support reproducibility and practical adoption, we provide a publicly available implementation of our approach.

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