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

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 mean-square error between the computed linear regression and the noisy sign...

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Bibliographic Details
Published in:2018 International Interdisciplinary PhD Workshop (IIPhDW) pp. 277 - 281
Main Authors: Kowalski, Pawel, Smyk, Robert
Format: Conference Proceeding
Language:English
Published: IEEE 01.05.2018
Subjects:
denoising filters
Gaussian filter
Kalman filter
Kalman filters
Kernel
kernel smoothing
least-square
mean filter
median filter
Microsoft Windows
Noise reduction
Savitzky-Golay
signal processing
Signal processing algorithms
smoothing
smoothing algorithms
Smoothing methods
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Summary: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 mean-square error between the computed linear regression and the noisy signal. Finally, we have compared mean, median, Savitzky-Golay, Kalman and Gaussian filter algorithms for the data from the digital sensor. The figure of merit was also the algorithm execution time.
DOI:10.1109/IIPHDW.2018.8388373