Barland Stéphane, Gustave François
Opt Express. 2021 Apr 12;29(8):11433-11444. doi: 10.1364/OE.419844.
Self-mixing interferometry is a well established interferometric measurement technique. In spite of the robustness and simplicity of the concept, interpreting the self-mixing signal is often complicated in practice, which is detrimental to measurement availability. Here we discuss the use of a convolutional neural network to reconstruct the displacement of a target from the self-mixing signal in a semiconductor laser. The network, once trained on periodic displacement patterns, can reconstruct arbitrarily complex displacement in different alignment conditions and setups. The approach validated here is amenable to generalization to modulated schemes or even to totally different self-mixing sensing tasks.
自混合干涉测量法是一种成熟的干涉测量技术。尽管该概念具有稳健性和简单性，但在实际中解读自混合信号通常很复杂，这不利于测量的可用性。在此，我们讨论使用卷积神经网络从半导体激光器中的自混合信号重建目标的位移。该网络一旦在周期性位移模式上进行训练，就能在不同的对准条件和设置下重建任意复杂的位移。这里验证的方法易于推广到调制方案，甚至完全不同的自混合传感任务。
