Spectral Graph Filtering for Noisy Signals Using the Kalman filter

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Published: Jun 27, 2023
DOI: https://doi.org/10.37936/ecti-eec.2023212.249818
Keywords:
Graph signal processing Graph Fourier transform Signal denoising, Kalman filter

Main Article Content

Nawar
Electrical Engineering Department, College of Engineering, Wasit University, Kut, Iraq
Asaad Taib
Electrical Engineering Department, College of Engineering, Wasit University, Kut, Iraq

Abstract

Noise is unwanted electrical or electromagnetic radiation that degrades the quality of the signal and the data. It can be difficult to denoise a signal that has been acquired in a noisy environment, but doing so may be necessary in a number of signal processing applications. This paper extends the issue of signal denoising from signals with regular structures, which are affected by noise, to signals with irregular structures by applying the graph signal processing (GSP) technique and a very wellknown filter, the standard Kalman filter, after adjusting it. When the modified Kalman filter is compared to the standard Kalman filter, the modified one performs better in situations where there are uncertain observations and/or processing noise and shows the best results. Also, the modified Kalman filter showed a higher efficiency when we compared it with other filters for different types of noise, which are not only standard Gaussian noises but also uniform distribution noise across two intervals for uncertain observation noise.

Article Details

How to Cite
Al-Attabi, A., & Al, A. (2023). Spectral Graph Filtering for Noisy Signals Using the Kalman filter. ECTI Transactions on Electrical Engineering, Electronics, and Communications, 21(2), 249818. https://doi.org/10.37936/ecti-eec.2023212.249818
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Vol. 21 No. 2 (2023): Regular Issue (June 2023)
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Author Biographies

Nawar, Electrical Engineering Department, College of Engineering, Wasit University, Kut, Iraq

Ali K. Nawar Al-Attabi Received the bachelor’s degree in electrical engineering from
Al-Mustansiria University, Baghdad, Iraq in 2009. and the Master’s degree in electrical
engineering from California State University, Fullerton in the USA in 2017. Since 2019,
he joined the department of electrical engineering at the University of Wasit, Iraq as a
lecturer. His main research interests include IoT, Signal Processing, Artificial Intelligence, and Communication

Asaad Taib, Electrical Engineering Department, College of Engineering, Wasit University, Kut, Iraq

Ali Asaad Taib Al Received his B.Sc. in Electrical Engineering/Electronics from Amirkabir University of Technology in 1996, his M.Sc. in Computer Engineering from the same institution in 2000, and his Ph.D. in the fields of deep learning and computer vision from the University of Central Florida in the United States in 2020. He joined the academic staff of Wasit University, Kut, Iraq, from 2005 to 2013 as an assistant lecturer, and from 2020 until present as a lecturer. His main research interests include Machine learning / Deep learning, Computer vision, Large scale data retrieval, and Quantum computing.

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