Pre-Processing of Signals Observed from Laser Diode Self-mixing Intereferometries using Neural Networks

L. Wei, University of Wollongong
J. Chicharo, University of Wollongong
Y. Yu, University of Wollongong
J. Xi, University of Wollongong

Article comments

This conference paper was originally published as Wei, L, Chicharo, J, Yu, Y, Xi, J, Pre-Processing of Signals Observed from Laser Diode Self-mixing Intereferometries using Neural Networks, IEEE International Symposium on Intelligent Signal Processing WISP 2007, 3-5 Oct, 1-5.

Abstract

This paper presents a novel neural network signal interpolation technique in order to eliminate the noise and disturbance associated with the self-mixing signal observed from optical feedback self- mixing interferometry (OFSMI). The proposed technique aims to improve the accuracy for displacement and moving track measurement of a target. The performance of the proposed approach is evaluated by both simulation and experimentation, with simulation revealing a measuring accuracy of A/25 for weak feedback and J20 for moderate feed back.