
Chebyshev filter and Butterworth filters: Comparison and applications in different cases
- 1 Civil Aviation University of China
* Author to whom correspondence should be addressed.
Abstract
This study focuses on the filter selection problem in signal processing and wireless communication applications. Specifically, addressed two different filter selection problems: the Chebyshev filter and the Butterworth filter. Using MATLAB, in order to design and model these two types of filters. The performance, frequency response, transition band width, filter order, design complexity and target application of Chebyshev filter and Butterworth filter under different parameters are analyzed and compared. The selection of the two filters under different conditions and the reasons for choosing them are discussed. The Chebyshev filter is suited for applications that call for a high frequency response, and Chebyshev filters provide a steeper roll-down slope, which can suppress high-frequency noise and interference signals, according to the test results, it is widely used in communication systems. On the other hand, the Butterworth filter is more suitable for applications that require a flat passband and a wide stopband, such as audio systems.
Keywords
Chebyshev Filter, Butterworth Filter, Digital Signal Processing
[1]. Jamalabadi A Y M. Analytical study of magnetohydrodynamic propulsion stability. Journal of Marine Science and Application,2014,13(3).
[2]. Mandal S,Ghoshal P S,Kar R, et al.Optimal Linear Phase Finite Impulse Response Band Pass Filter Design Using Craziness Based Particle Swarm Optimization Algorithm. Journal of Shanghai Jiaotong University (Science),2011,16(06):696-703.
[3]. Wei Yewen, Li Yingzhi, Cao Bin, et al. Research on Low Power Consumption and Energy Balance Technology of Lithium Batteries with Buck Circuit. Journal of Electrical Technology, 2018,33 (11): 2575-2583.
[4]. Liu Yan, Zhang Suying. The ground states and pseudo textures of rotating two component Bose - Einstein condensates trapped in harmonic plus quartz potential. Chinese Physics B, 2016,25 (09): 223-230
[5]. Chiacchiari L,Loprencipe G.Measurement methods and analysis tools for rail irregularities:a case study for urban tram track. Journal of Modern Transportation,2015,23(02):137-147.
[6]. Yang Ziwen, Zhang Zijian, Zhu Yanmeng, et al. Design of bandpass filters for power supply and communication multiplexing circuits. Magnetic Materials and Devices, 2021,52 (05): 79-83.
[7]. Gao S,Xue B,Li J, et al. High-resolution data acquisition technique in broadband seismic observation systems. Science China Technological Sciences,2016,59(6).
[8]. Ye Zhan Design and Implementation of Programmable Gain Amplifiers and Current Mode Complex Bandpass Filters. Southeast University, 2015.
[9]. Mayashi Performance Analysis and Optimization of Filtering Multi tone Modulation System. Chongqing University of Posts and Telecommunications, 2017.
[10]. Xing Y, Lv C, Chen L, et al. Advances in Vision-Based Lane Detection: Algorithms, Integration, Assessment, and Perspectives on ACP-Based Parallel Vision. IEEE/CAA Journal of Automatica Sinica,2018,5(03):645-661.
[11]. MENG B, XU H, RUAN J, et al.Theoretical and experimental investigation on novel 2D maglev servo proportional valve. Chinese Journal of Aeronautics,2021,34(04):416-431.
[12]. Two examples of using physical mechanics approach to evaluate colloidal stability. Science China (Physics,Mechanics & Astronomy),2012,55(06):933-939.
Cite this article
Li,Z. (2024). Chebyshev filter and Butterworth filters: Comparison and applications in different cases. Applied and Computational Engineering,38,102-111.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).