Discrete Wavelet Transform for Laser Diode Self-Mixing Interferometry Processing for Vibrations Measurement

Optical feedback interferometry, known for its efficient resource usage, contrasts traditional two-beam interferometry. Laser diode self-mixing interferometry (SM) is a prevalent remote sensing approach. Despite providing insights into target and laser diode dynamics, noise compromises SM interferometry’s efficacy. Leveraging time-frequency signal processing reduces noise in self-mixing signals. This paper presents a comprehensive investigation into the processing of self-mixing laser sensor signals, specifically focusing on leveraging the discrete wavelet transform (DWT) for enhanced analysis. A Wavelet Transform-based Phase Unwrapping Technique is introduced, surpassing traditional methods like the Fast Fourier Transform, especially in handling non-stationary self-mixing (SM) signals. The technique’s key strength lies in its ability to simultaneously retain frequency and temporal information, effectively addressing challenges associated with dynamic signals. The proposed method ensures precise outcomes for stationary and non-stationary signals, marking a significant advancement in laser sensor signal processing. By harnessing DWT’s capabilities, the approach offers heightened accuracy and versatility, shaping the trajectory of SM signal analysis. Incorporating the Discrete Wavelet Transform (DWT) for SMI, our study achieved remarkable accuracy improvements. DWT-based processing resulted in an error of 12.77nm as compared to 60.98nm for RPUM, 5.95nm for FFT for stationary signals, and 11.98nm as compared to 64.64nm for RPUM and 1228nm for FFT for non-stationary signals, marking a significant advancement in laser sensor signal processing. Through this work, we provide a transformative avenue for advancing laser sensor research and elevating signal processing methodologies across disciplines.