Emd-based filtering using the Hausdorff distance

Abstract

This paper introduces a new signal-filtering method, which combines the empirical mode decomposition (EMD) and the Hausdorff distance (ℌD). A noisy signal is adaptively broken down into oscillatory components called intrinsic mode functions (IMFs) by EMD followed by an estimation of the probability density function (pdf) of each extracted mode. The filtered signal is obtained by performing a partial reconstruction using a selected set of empirical modes (termed relevant modes). The article focuses on an intuitive geometrical approach to identify the relevant modes based on the ℌD between the pdf of the noisy signal and that of each mode. Obtained results of the new filtering are compared to those of the EMD-CMSE (Consecutive Minimum Square Error) [1], the White Noise characteristics approach EMD-WN [2] and the Selection Criterion approach EMD-SC [3]. Dn different signals with varying signal-to-noise ratio values are used to validate the obtained results. The study is limited to signals corrupted by additive white Gaussian noise.