Power Spectrum Estimation Method Based on Matlab
Authors: 
Xiaobo Jin, Yuwen Wang, and Wenjun HongAuthors Info & Claims
Xiaobo Jin, Yuwen Wang, and Wenjun HongAuthors Info & ClaimsAugust 2019
Article No.: 70, Pages 1 - 5
Abstract
Power spectrum estimation is one of the important research contents of digital signal processing. Power spectrum estimation is divided into classic power spectrum estimation and modern power spectrum estimation. Modern power spectrum estimation is proposed for the shortcomings of classical power spectrum estimation. The principles of the periodogram method, the improved welch method and the AR model method in the classic power spectrum estimation, Matlab simulations were carried out, and their characteristics were analyzed and compared. It was found that the Burg method of the AR parameter model is better. The classic power spectrum estimation has large variance and low spectral resolution, but modern power spectrum estimation is not affected by the window function, so it has higher spectral resolution and smooth spectral curve.
References
[1]
Hu Guangshu, digital signal processing theory, algorithm and implementation. Tsinghua University Press. Year 2003.
[2]
Holzman E L.A Wide Band TEM Horn Array Radiator with a Novel Microstrip Feed [A]. IEEE International Conference on Phased Array Systems and Technology [C].2000: 441--444.
[3]
Jun Yao, Guihua Zeng. Key agreement and identity authentication protocols for ad hoc networks, ITCC 2004. International Conference on Information Technology: Coding and Computing, 2004, Vol.2
[4]
Kanda M. The Effects of Resistive Loading of TEM Horns [J]. IEEE Trans. on Electromagnetic Compatibility, 1982 (2): 245--255.
[5]
Lu Huaguang, Peng Xueyu. Random signal processing [M]. Xi'an: Xi'an University of Electronic Science and Technology Press, 2003.
[6]
Robert J. Scilling, Sandra L. Harris. Fundamentals of digital pressing using MATLAB. Xi'an: Xi'an Jiao tong university, 2005.
[7]
Wei Xin, Zhang Ping. Analysis of window function in periodogram methodpower spectrum estimation [J]. Modern Electronic Technology, 2005, 28 (3): 20--21.
[8]
Xu Kejun. Signal analysis and processing. Tsinghua University Press. year 2006.
[9]
Yi Mu, Willy Susilo, Yan-Xia Lin, et al. Identity-Based Authenticated Broadcast Encryption and Distributed Authenticated Encryption, Lecture Notes in Computer Science, 2004, Volume 3321
[10]
Zhang Licai, Wang Min. digital signal processing [M]. Beijing: People's Posts and Telecommunications Press. 2008
Index Terms
- Power Spectrum Estimation Method Based on Matlab
Recommendations
Wideband Power Spectrum Sensing for Cognitive Radios Based on Sub-Nyquist Sampling
In a cognitive radio system, efficient wideband spectrum estimation is a basic component of dynamic spectrum access. The systems high sampling rate is the main challenge in the frontend. In this paper, wideband power spectrum sensing is studied based on ...
Spectrum sensing exploiting the maximum value of power spectrum density in wireless sensor network
Spectrum sensing plays a foundational role in cognitive radio sensor networks. However, only the methods with low computational complexity can be utilized due to energy restriction of sensor node. To this end, a novel frequency-domain spectrum sensing ...
A Modification to the Current Algorithm of the Correlation Method for Cross Power Spectrum Estimation
ICECE '10: Proceedings of the 2010 International Conference on Electrical and Control EngineeringThe correlation method for cross power spectrum estimation based on FFT is studied in this paper. A modification is made to the current algorithm of the correlation method. Numerical examples show the correctness of the modification.
Comments
Information & Contributors
Information
Published In
August 2019
584 pages
ISBN:9781450376259
DOI:10.1145/3387168
Copyright © 2019 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 25 May 2020
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ICVISP 2019
ICVISP 2019: 3rd International Conference on Vision, Image and Signal Processing
August 26 - 28, 2019
BC, Vancouver, Canada
Acceptance Rates
ICVISP 2019 Paper Acceptance Rate 126 of 277 submissions, 45%;
Overall Acceptance Rate 186 of 424 submissions, 44%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 134Total Downloads
- Downloads (Last 12 months)28
- Downloads (Last 6 weeks)1
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in