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.