Review and comparison of smoothing algorithms for one-dimensional data noise reduction

Paweł Kowalski; Robert Smyk

doi:10.1109/iiphdw.2018.8388373

Review and comparison of smoothing algorithms for one-dimensional data noise reduction

Abstract

The paper considers the choice of parameters of smoothing algorithms for data denoising. The impact of the window size on smoothing accuracy was analyzed. The parameters of denoising filters were selected with respect to the meansquare error between the computed linear regression and the noisy signal. Finally, we have compared mean, median, SavitzkyGolay, Kalman and Gaussian filter algorithms for the data from the digital sensor. The figure of merit was also the algorithm execution time.

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Keywords

NOISE REDUCTION, SMOOTHING, SMOOTHING ALGORITHMS, MEAN FILTER, MEDIAN FILTER, LEAST-SQUARE, SAVITZKY-GOLAY, KALMAN FILTER, GAUSSIAN FILTER, KERNEL SMOOTHING, SIGNAL PROCESSING, DENOISING FILTERS

Details

Category:: Conference activity
Type:: publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
Title of issue:: 2018 International Interdisciplinary PhD Workshop (IIPhDW) strony 277 - 281
Language:: English
Publication year:: 2018
Bibliographic description:: Kowalski P., Smyk R.: Review and comparison of smoothing algorithms for one-dimensional data noise reduction// 2018 International Interdisciplinary PhD Workshop (IIPhDW)/ : , 2018, s.277-281
DOI:: Digital Object Identifier (open in new tab) 10.1109/iiphdw.2018.8388373
Verified by:: Gdańsk University of Technology