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

尤亚强; 李鑫涛; 刘晖; 和丹

doi:10.11884/HPLPB202436.240125

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

doi: 10.11884/HPLPB202436.240125

西安工程大学机电工程学院，西安 710048

基金项目: 陕西省秦创原“科学家+工程师”队伍项目(2023KXJ-129)；西安工程大学柯桥纺织产业创新研究院暨西安工程大学（柯桥）研究生创新学院产学研协同创新项目（19KQYB12）

详细信息

作者简介:
尤亚强，1115646634@qq.com

通讯作者:
刘　晖，huiliu@xpu.edu.cn。

中图分类号: TN247
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- PDF下载量: 29
- 被引次数: 0
出版历程
- 收稿日期: 2024-04-12
- 修回日期: 2024-06-12
- 录用日期: 2024-06-12
- 网络出版日期: 2024-06-20
- 刊出日期: 2024-07-04

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

School of Mechanical and Electrical Engineering, Xi’an Polytechnic University, Xi’an 710048, China

摘要

摘要: 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Savitzky-Golay（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。
- 自混合干涉 /
- 相位展开 /
- 位移重构 /
- 小波阈值滤波 /
- Savitzky-Golay滤波
Abstract: 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.
- self mixing interference /
- phase unwrapping /
- displacement reconstruction /
- wavelet threshold filter /
- Savitzky-Golay filter

HTML全文

图 1 软阈值和硬阈值函数

Figure 1. Soft threshold and hard threshold functions

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图 2 信号处理流程图

Figure 2. Signal processing flowchart

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图 3 激光自混合干涉实验装置

Figure 3. Experimental setup for laser self mixing interference

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图 4 压电陶瓷的调制电压和实验SMI信号

Figure 4. Modulation voltage of PZT and experimental SMI signal

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图 5 小波分解的低频近似系数和高频细节系数

Figure 5. Low frequency approximation coefficients and high frequency detail coefficients of wavelet decomposition

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图 6 小波阈值滤波后SMI信号的局部对比

Figure 6. Comparison of SMI local signals after wavelet threshold filtering

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图 7 基于小波阈值滤波的SMI信号峰谷值检测

Figure 7. Peak valley detection of SMI signals based on wavelet threshold filtering

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图 8 小波软阈值滤波与S-G滤波相结合后SMI信号的寻峰图

Figure 8. Peak finding diagram of SMI signal after combining wavelet threshold filtering and S-G filtering

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图 9 小波硬阈值滤波、小波软阈值滤波、小波软阈值滤波结合S-G滤波的SMI归一化重构位移与归一化PZT驱动电压对比

Figure 9. Comparison of normalized displacement and normalized PZT driving voltage for SMI denoising using wavelet hard thresholding, wavelet soft thresholding, wavelet soft thresholding with S-G filtering

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参考文献(24)

[1]	Taimre T, Nikolić M, Bertling K, et al. Laser feedback interferometry: a tutorial on the self-mixing effect for coherent sensing[J]. Advances in Optics and Photonics, 2015, 7(3): 570-631. doi: 10.1364/AOP.7.000570
[2]	Brambilla M, Columbo L L, Dabbicco M, et al. Versatile multimodality imaging system based on detectorless and scanless optical feedback interferometry-a retrospective overview for a prospective vision[J]. Sensors, 2020, 20: 5930. doi: 10.3390/s20205930
[3]	Bhardwaj V K, Maini S. Compact and self-aligned fluid refractometer based on the Doppler-induced self-mixing effect[J]. Applied Optics, 2020, 59(10): 3064-3072. doi: 10.1364/AO.388078
[4]	Donati S, Norgia M. Overview of self-mixing interferometer applications to mechanical engineering[J]. Optical Engineering, 2018, 57: 051506.
[5]	Kou Ke, Li Xingfei, Li Li, et al. Absolute distance estimation with improved genetic algorithm in laser self-mixing scheme[J]. Optics & Laser Technology, 2015, 68: 113-119.
[6]	郭冬冬, 叶会英. 光反馈自混合干涉位移实时跟踪测量算法[J]. 激光杂志, 2017, 38(1):55-59 Guo Dongdong, Ye Huiying. Optical feedback self-mixing interference displacement real-time tracking and measurement algorithm[J]. Laser Journal, 2017, 38(1): 55-59
[7]	Liu Hui, Li Sijia, You Yaqiang, et al. Model of multiple mode gain competition in self-mixing laser diode[J]. Optik, 2023, 281: 170853. doi: 10.1016/j.ijleo.2023.170853
[8]	Wei Zheng, Huang Wencai, Zhang Jie, et al. Obtaining scalable fringe precision in self-mixing interference using an even-power fast algorithm[J]. IEEE Photonics Journal, 2017, 9: 6803211.
[9]	宋观平, 齐攀, 李莹, 等. 基于双光反馈的双偏振差分自混合干涉降噪技术[J]. 激光与光电子学进展, 2022, 59:1126001 Song Guanping, Qi Pan, Li Ying, et al. Dual-polarization differential noise reduction technology in dual-beam feedback self-mixing interferometer[J]. Laser & Optoelectronics Progress, 2022, 59: 1126001
[10]	张玉燕, 周航, 闫美素. 基于经验模态分解的自混合干涉相位提取方法研究[J]. 物理学报, 2015, 64:054203 doi: 10.7498/aps.64.054203 Zhang Yuyan, Zhou Hang, Yan Meisu. Study on the phase-extracting method of self-mixing signal based on empirical mode decomposition[J]. Acta Physica Sinica, 2015, 64: 054203 doi: 10.7498/aps.64.054203
[11]	宦海, 郭克伦, 张雨, 等. 两路反馈外腔自混合干涉信号的相位提取方法[J]. 激光与光电子学进展, 2016, 53:061203 Huan Hai, Guo Kelun, Zhang Yu, et al. Phase-extracting method of laser self-mixing interference signal with two feedback external cavity[J]. Laser & Optoelectronics Progress, 2016, 53: 061203
[12]	姜春雷, 周旭明. 基于激光自混合干涉技术和小波变换的齿轮箱故障诊断[J]. 光学技术, 2017, 43(1):83-86 Jiang Chunlei, Zhou Xuming. Application of laser self-mixing interference technology and wavelet transform in gearbox fault diagnosis[J]. Optical Technique, 2017, 43(1): 83-86
[13]	郭晴, 叶会英. 基于奇异值分解的自混合干涉信号降噪方法[J]. 现代电子技术, 2019, 42(9):26-30 Guo Qing, Ye Huiying. Singular value decomposition based denoising method of self-mixing interference signal[J]. Modern Electronics Technique, 2019, 42(9): 26-30
[14]	Liu Hui, You Yaqiang, Li Sijia, et al. Denoising of laser self-mixing interference by improved wavelet threshold for high performance of displacement reconstruction[J]. Photonics, 2023, 10: 943. doi: 10.3390/photonics10080943
[15]	邢挺, 范增盛, 马君梁, 等. 基于小波变换改进阈值函数的故障行波去噪方法[J]. 电工技术, 2023(17):37-43 Xing Ting, Fan Zengsheng, Ma Junliang, et al. Denoising method of fault traveling wave based on wavelet transform and improved threshold function[J]. Electric Engineering, 2023(17): 37-43
[16]	张宝峰, 左铭, 朱均超, 等. 基于VMD与小波阈值的激光自混合干涉位移信号滤波方法[J]. 激光杂志, 2021, 42(2):77-82 Zhang Baofeng, Zuo Ming, Zhu Junchao, et al. Research on laser self-mixing interference displacement signal filtering method based on VMD and wavelet threshold[J]. Laser Journal, 2021, 42(2): 77-82
[17]	郝军, 李福生, 杨婉琪, 等. Russian roulette优化小波算法在X射线荧光光谱去噪中的应用[J]. 激光与光电子学进展, 2023, 60:0930006 Hao Jun, Li Fusheng, Yang Wanqi, et al. X-ray fluorescence spectral denoising analysis based on the Russian roulette optimized wavelet algorithm[J]. Laser & Optoelectronics Progress, 2023, 60: 0930006
[18]	罗会甫, 王扬红, 朱炜, 等. 基于激光自混合干涉法的微振动测量[J]. 北京理工大学学报, 2017, 37(6):584-589 Luo Huifu, Wang Yanghong, Zhu Wei, et al. Measurement of micro-vibration based on laser self-mixing interference[J]. Transactions of Beijing Institute of Technology, 2017, 37(6): 584-589
[19]	张震川, 曹保锋, 李鹏, 等. 小波包分形在远区核爆电磁脉冲识别中的应用[J]. 强激光与粒子束, 2022, 34:066002 doi: 10.11884/HPLPB202234.210375 Zhang Zhenchuan, Cao Baofeng, Li Peng, et al. Recognition of far-region nuclear electromagnetic pulse based on wavelet fractal technique[J]. High Power Laser and Particle Beams, 2022, 34: 066002 doi: 10.11884/HPLPB202234.210375
[20]	李思嘉, 刘晖, 熊玲玲, 等. 基于S-G滤波与包络提取算法的半导体激光器自混合干涉微位移测量[J]. 机械与电子, 2022, 40(4):13-19 Li Sijia, Liu Hui, Xiong Lingling, et al. Micro-displacement measurement of semiconductor laser self-mixing interference based on S-G Filter and envelope extraction algorithm[J]. Machinery & Electronics, 2022, 40(4): 13-19
[21]	王婧瑶, 王红军. 基于Mask R-CNN与SG滤波的手势识别关键点特征提取方法[J]. 电子测量与仪器学报, 2021, 35(9):41-48 Wang Jingyao, Wang Hongjun. Gesture key point extraction method based on Mask R-CNN and SG filter[J]. Journal of Electronic Measurement and Instrumentation, 2021, 35(9): 41-48
[22]	Wang Xiufang, Yuan Ye, Chen Peng, et al. Laser self-mixing based on peak–valley point detection algorithm for displacement reconstruction[J]. Optical and Quantum Electronics, 2020, 52: 34. doi: 10.1007/s11082-019-2153-9
[23]	Zhao Yu, Li Jiawei, Zhang Menglei, et al. Phase-unwrapping algorithm combined with wavelet transform and Hilbert transform in self-mixing interference for individual microscale particle detection[J]. Chinese Optics Letters, 2023, 21: 041204. doi: 10.3788/COL202321.041204
[24]	王华英, 于梦杰, 刘飞飞, 等. 基于快速傅里叶变换的四种相位解包裹算法[J]. 强激光与粒子束, 2013, 25(5):1129-1133 doi: 10.3788/HPLPB20132505.1129 Wang Huaying, Yu Mengjie, Liu Feifei, et al. Four phase unwrapping algorithms based on fast Fourier transform[J]. High Power Laser and Particle Beams, 2013, 25(5): 1129-1133 doi: 10.3788/HPLPB20132505.1129