Data-Driven Multivariate Signal Denoising Using Mahalanobis Distance

Naveed Ur Rehman*, Khuram Naveed, Bushra Khan

*Corresponding author af dette arbejde

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

Abstract

A novel multivariate signal denoising method is presented that computes Mahalanobis distance measure at multiple data scales obtained from multivariate empirical mode decomposition (MEMD) algorithm. That enables joint multichannel data denoising directly in multidimensional space where input signal resides, by employing interval thresholding on multiple data scales in . We provide theoretical justification of using Mahalanobis distance at multiple scales obtained from MEMD and prove that the proposed method is able to incorporate inherent correlation between multiple data channels in the denoising process. The performance of the proposed method is verified on a range of synthetic and real world signals.
OriginalsprogEngelsk
TidsskriftIEEE Signal Processing Letters
Vol/bind26
Nummer9
Sider (fra-til)1408-1412
ISSN1070-9908
DOI
StatusUdgivet - sep. 2019
Udgivet eksterntJa

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