Removing the impulsive noise contained in a self-mixing interferometry system using outlier detection

Abstract

Impulsive noise is a major problem that seriously degrades the performance of self-mixing interferometry (SMI). A new method to rectify this issue is proposed. First, an outlier detection approach is employed to detect the data samples corrupted by the impulsive noise, and then the SMI signal waveform is rectified by means of least square (LS) curve fitting. The results show that the proposed method can effectively remove the impulsive noise without introducing distortion to the original waveform and thus lead to improvement in the performance of an SMI system.