Signal Smoothing with Time-Space Fractional Order Model

Authors

  • Yuanlu Li Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science & Technology, No.219 Ningliu Road, 210044, Nanjing, China; School of Automation, Nanjing University of Information Science & Technology, No.219 Ningliu Road, 210044, Nanjing, China https://orcid.org/0000-0003-3237-7235

DOI:

https://doi.org/10.2478/msr-2021-0004

Keywords:

fractional diffusion, signal smoothing, filtering

Abstract

The time-space fractional-order model (TSFOM) is a generation of the classical diffusion model which is an excellent smoothing method. In this paper, the fractional-order derivative in the model is found to have good performance for peak-preserving. To check the validity and performance of the model, some noisy signals are smoothed by some commonly used smoothing methods and results are compared with those of the proposed model. The comparison result shows that the proposed method outperforms the classical nonlinear diffusion model and some commonly used smoothing methods.

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Published

30.03.2021

How to Cite

Li, Y. (2021). Signal Smoothing with Time-Space Fractional Order Model. Measurement Science Review, 21(1), 25–32. https://doi.org/10.2478/msr-2021-0004

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