Measuring parameters of laser self-mixing interferometry sensor based on back propagation neural network

Abstract

Self-mixing interferometry (SMI) is a well-known non-destructive sensing technique that has been widely applied in both laboratory and engineering applications. In a laser SMI sensing system, there are two vital parameters, i.e., optical feedback factor C and line-width enhancement factor α, which influence the operation characteristics of the laser as well as the sensing performance. Therefore, many efforts have been made to determine them. Most of the existing methods of estimating these two parameters can often be operated in a certain feedback regime, e.g., weak or moderate feedback regime. In this paper, we propose a new method to estimate C and α based on back-propagation neural network for all feedback regimes. A parameter predicting model was trained and built. The performance of the proposed predicting model was tested using simulation and experiment data. The results show that the proposed method can estimate C and α with an average error of 2.76% and 2.99%, respectively. Additionally, the proposed method is noise-proof. The method and results are useful for extending the utilization of SMI technology in practical engineering fields.

Measurement of Plane Acoustic Waves Using an Optical Feedback Interferometer

“…Several methods are described in the literature for the characterization of C and α when an OFI is used as a vibrometer [14,[16][17][18]. The method used in this work is the one described by Ri et al [11] which has several advantages:…”

Self-Mixing Interferometer for Acoustic Measurements through Vibrometric Calibration

“…Parameters C and can be estimated from SMI vibrometric signals in air at rest [ 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ]. Among the different methods, some are only effective in weak feedback [ 23 , 24 , 25 ] and others only in moderate feedback [ 26 , 27 , 28 ].…”

“…For instance, if the distance between the LD and the retro-reflective surface is about 20 cm, an acoustic wave at 112 dB SPL induces an optical path variation of 1.2 nanometers [ 13 ], which is three orders of magnitude below mechanical vibrations of a few micrometers [ 3 ]. For the measurement of C and in strong feedback regime, the methods can be divided into two categories: phase unwrapping methods [ 29 , 30 , 31 , 33 ] and direct SMI signal analysis methods [ 32 , 34 , 35 ].…”

“…This method can be used to determine C values ranging from 1 to 7 and from 2 to 4.9. An and Liu [ 34 ] trained a neural network with SMI signals to estimate the values of C and corresponding to each of them. After training, this network was able to estimate C between 0.1 and 10 and between 2 and 7 from a given SMI signal.…”

High precision estimation of optical feedback factor and linewidth enhancement using non-dominated sequential genetic algorithm II (NSGA-II)

“…Therefore, the estimation methods of C and α have become a hot topic in the field of SMI research. Numerous related studies have been conducted, covering different ranges of feedback intensity with different accuracies, e.g., fitting methods [4], feature extraction methods, relaxation oscillation frequencies [5], and deep learning [6].…”