Joint estimation of self-mixing interferometry parameters and displacement reconstruction based on local normalization

Abstract

It is not easy to estimate self-mixing interferometry parameters, namely, the optical feedback factor and the linewidth enhancement factor from the self-mixing signals (SMSs) affected by noise such as speckle. These SMSs call for normalization, which is not only difficult, but also apt to distort the intrinsic information of the signals, thereby resulting in incorrect estimation of the parameters and the displacement reconstruction. In this paper, we present what we believe is a novel normalization method we call “local normalization,” which enables more exact and simpler estimation and displacement retrieval compared to previous methods, for it is based on an analytic relation instead of approximation. The method is very noise-proof, and especially speckle-noise-proof as well. The method proposed can be applied to moderate and strong feedback regimes. The simplicity and accuracy of the method will provide a fine tool for a low-cost self-mixing displacement sensor with a high resolution of about 40 nm.

Phase-unwrapping algorithm combined with wavelet transform and Hilbert transform in self-mixing interference for individual microscale particle detection

“…Besides measuring temporal signal waveform fringe number [18] and frequency spectral width [7] , novel precise alternatives for particle detection are still required. As a kind of phase observation tool, the phase-unwrapping method (PUM) has been exhaustively studied in SMI sensor systems [19][20][21][22][23][24][25][26][27] , and the spatial resolution even can reach λ=67 [28] . This processing method normally involves two steps: the first step is phase retrieval, and the second step is to estimate the feedback factor and linewidth enhancement factor for phase profile correction and displacement reconstruction.…”