Data-Driven Multivariate Signal Denoising Using Mahalanobis Distance

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 \mathcal {R}^N wher...

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Bibliographic Details
Published in:IEEE signal processing letters 2019-09, Vol.26 (9), p.1408-1412
Main Authors: Rehman, Naveed ur, Khan, Bushra, Naveed, Khuram
Format: Article
Language:English
Subjects:
Algorithms
Balances (scales)
Correlation
Covariance matrices
denoising
Distance measurement
Gaussian noise
mahalanobis distance
Multivariate analysis
multivariate empirical mode decomposition
Multivariate signals
Noise reduction
Signal denoising
Signal processing algorithms
Thresholding (Imaging)
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Summary: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 \mathcal {R}^N where input signal resides, by employing interval thresholding on multiple data scales in \mathcal {R}^N. 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.
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2019.2932715