基于小波阈值滤波和S-G滤波相结合的激光自混合干涉微位移重构

Micro displacement reconstruction of laser self mixing interference based on wavelet threshold filtering and S-G filtering

在半导体激光器自混合干涉(SMI)微位移测量中,相位信息的准确提取对于位移的高精度重构至关重要。然而,测量噪声会对SMI信号引入相位误差,位移重构精度较低。针对信号去噪问题,小波阈值去噪算法能够滤除多数信号的噪声,但该算法在SMI信号去噪中存在局部振荡的问题,使得去噪后的自混合干涉信号中出现新的干扰峰,最终识别到的干扰峰会产生错误的位移重构。提出一种基于小波阈值滤波和SavitzkyGolay(S-G)滤波相结合的SMI信号处理算法。该算法通过引入S-G滤波算法,从全局上平滑处理相位跳变点的噪声,解决了只是采用小波去噪算法而引起SMI信号的局部振荡问题。位移重构实验结果表明,基于小波阈值结合S-G滤波算法的激光自混合干涉重构信号消除了振幅处和相位跳变点之间的高频噪声,重构位移曲线保持了振动物体的原始波形特征。

In semiconductor laser self-mixing interferometry(SMI)for micro-displacement measurement,the precise extraction of phase information is essential for high-accuracy displacement reconstruction.However,measurement noise induces phase errors in the SMI signal,leading to suboptimal displacement reconstruction accuracy.To tackle the challenge of signal denoising,wavelet thresholding denoising algorithms can effectively filter out most of the noise.However,they suffer from local oscillation issues when applied to SMI signal denoising.This results in the appearance of new interference peaks in the denoised self-mixing interference signal,thereby causing erroneous displacement reconstruction.This paper proposes an SMI signal processing algorithm that synergistically combines wavelet thresholding and Savitzky-Golay(S-G)filtering.By incorporating the S-G filtering algorithm,the algorithm smooths out noise at phase jump points on a global scale,thus mitigating the local oscillation issues inherent in wavelet-only denoising.Experimental results of displacement reconstruction indicate that the proposed method successfully eliminates high-frequency noise at both amplitude and phase jump points.Consequently,the reconstructed displacement curve retains the original waveform characteristics of the vibrating object.