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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Vydáno v:2018 International Interdisciplinary PhD Workshop (IIPhDW) s. 277 - 281
Hlavní autoři: Kowalski, Pawel, Smyk, Robert
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.05.2018
Témata:
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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Shrnutí: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