Wavelet based multivariate signal denoising using Mahalanobis distance and EDF statistics

TL;DR

This paper introduces a multivariate signal denoising technique that combines wavelet transforms with a novel multivariate goodness of fit test based on Mahalanobis distance and EDF statistics to effectively identify and remove noise.

Contribution

It presents a new multivariate GoF test applied at multiple wavelet scales using Mahalanobis distance and EDF, enabling improved denoising of multivariate signals.

Findings

Effective noise removal demonstrated on synthetic datasets

Improved signal quality on real-world data

Robustness across different multivariate signals

Abstract

A multivariate signal denoising method is proposed which employs a novel multivariate goodness of fit (GoF) test that is applied at multiple data scales obtained from discrete wavelet transform (DWT). In the proposed multivariate GoF test, we first utilize squared Mahalanobis distance (MD) measure to transform input multivariate data residing in M-dimensional space $\mathcal{R}^M$ to a single-dimensional space of positive real numbers $\mathcal{R}_+$, i.e., $\mathcal{R}^M \rightarrow \mathcal{R}_+$, where $M > 1$. Owing to the properties of the MD measure, the transformed data in $\mathcal{R}_+$ follows a distinct distribution. That enables us to apply the GoF test using statistic based on empirical distribution function (EDF) on the resulting data in order to define a test for multivariate normality. We further propose to apply the above test locally on multiple input data scales obtained from discrete wavelet transform, resulting in a multivariate signal denoising framework. Within the proposed method, the reference cumulative distribution function (CDF) is defined as a quadratic transformation of multivariate Gaussian random process. Consequently, the proposed method checks whether a set of DWT coefficients belong to multivariate reference distribution or not; the coefficients belonging to the reference distribution are discarded. The effectiveness of our proposed method is demonstrated by performing extensive simulations on both synthetic and real world datasets.