Work

Neural Network-Based Estimation of Optical Feedback Factor in Self-Mixing Interferometry Systems Across Various Feedback Regimes

Year: 2025

Type: article

Abstract: Self-mixing interferometry (SMI) system is a promising and low-cost approach for displacement measurements. The resolution and precision of displacement estimation depend on key parameters of the SMI system and, in particular, on the optical feedback factor (C). This paper presents a new method for estimating thisCfactor based on an artificial neural network that analyzes certain specific fringe shape features of SMI signals. Specifically, only six features of the SMI signal are taken into account as input to a compact neural network with one hidden neuron layer (14 neurons) by the estimation process, which reduces the computational cost and simplifies its deployment. This study evaluates theC-estimation capability of the method in various simulated and experimental scenarios, including variations in displacement amplitude and frequency, random displacements, noise levels and speckle effects. These analyses show that the method works correctly for a moderate feedback regime (1CC≥ 1.5. For the strong feedback regime (C> 4.6), this error is less than 2% (forC< 8). Comparisons with other methods show similar or better accuracy but with the advantage of being able to handle all displacement waveforms (sinusoidal or arbitrary) at low computational cost, which facilitates integration into a real-time embedded system.

Source: IEEE Transactions on Instrumentation and Measurement

DOI: 10.1109/tim.2025.3580871

Authors: Mathieu Feuilloy, Guy Plantier, Francis Jayat, Olivier Bernal