Liang Hongwei, Chen Minghu, Jiang Chunlei, Kan Lingling, Shao Keyong
School of Electrical Information Engineering, Northeast Petroleum University, Daqing 163318, China.
Sensors (Basel). 2022 Aug 18;22(16):6171. doi: 10.3390/s22166171.
To measure the vibration of a target by laser self-mixing interference (SMI), we propose a method that combines feature extraction and random forest (RF) without determining the feedback strength (). First, the temporal, spectral, and statistical features of the SMI signal are extracted to characterize the original SMI signal. Secondly, these interpretable features are fed into the pretrained RF model to directly predict the amplitude and frequency ( and ) of the vibrating target, recovering the periodic vibration of the target. The results show that the combination of RF and feature extraction yields a fit of more than 0.94 for simple and quick measurement of and of unsmooth planar vibrations, regardless of the feedback intensity and the misalignment of the retromirror. Without a complex optical stage, this method can quickly recover arbitrary periodic vibrations from SMI signals without , which provides a novel method for quickly implementing vibration measurements.
为了通过激光自混合干涉（SMI）测量目标的振动，我们提出了一种结合特征提取和随机森林（RF）的方法，而无需确定反馈强度（）。首先，提取SMI信号的时间、频谱和统计特征以表征原始SMI信号。其次，将这些可解释的特征输入到预训练的RF模型中，以直接预测振动目标的振幅和频率（和），恢复目标的周期性振动。结果表明，RF和特征提取的结合对于简单快速地测量不平滑平面振动的和具有超过0.94的拟合度，而与反馈强度和后向反射镜的未对准无关。无需复杂的光学平台，该方法可以从SMI信号中快速恢复任意周期性振动，而无需，这为快速实现振动测量提供了一种新方法。
