International Journal of Engineering and Manufacturing (IJEM)
IJEM Vol. 10, No. 3, 8 Jun. 2020
Cover page and Table of Contents: PDF (size: 967KB)
A Methodical Study for Time-Frequency Analysis Model with Experimental Case Study on Chirp Signal
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Author(s)
Index Terms
Chirp Signal, Sunspot Signal, time-frequency Analysis, Short-Time Fourier transform (STFT), Wigner-Ville distribution (WVD), Hamming Window, non-stationary signals.
Abstract
In this paper, we are reporting on the comprehensive model design for time-frequency analysis system using Short-Time Fourier Transform (STFT) and Wigner-Ville Distribution (WVD) methods. As a case study, both STFT and WVD based time-frequency transforms have been developed via MATLAB platform and applied for both Chirp and Sunspot signals. The developed model considers the use of hamming moving window of length L=50 with 90% overlapping between the current and previous window positions. The simulation results showed that WVD is more accurate method for time and frequency analysis than STFT since it can provide simultaneous localization in both time and frequency with higher resolution than STFT which can only provide localization in either time or frequency at the same time. Also, the applied techniques provide an adequate distribution of time-frequency analysis only if they used with a non-stationary signal such as Chirp signal.
Cite This Paper
Qasem Abu Al-Haija. “A Methodical Study for Time-Frequency Analysis Model with Experimental Case Study on Chirp Signal", International Journal of Engineering and Manufacturing (IJEM), Vol.10, No.3, pp.1-11, 2020. DOI: 10.5815/ijem.2020.03.01
Reference
[1]Debnath L., The Wigner-Ville Distribution and Time-Frequency Signal Analysis. In Wavelet Transforms and Their Applications. Birkhauser, Boston, MA, (2002). Retrieved on-line from: https://doi.org/10.1007/978-1-4612-0097-0 5
[2]Patrick F., Time frequency and chirps. Proc. SPIE 4391, Wavelet Applications VIII, (2001). Retrieved on-line from: https://doi.org/10.1117/12.421196
[3]Sejdic E., Djurovic I. and Jiang J., Time-frequency feature representation using energy concentration: An overview of recent advances. Digital Signal Processing. 19 (1), (2009). Retrieved on-line from: https://doi.org/10.1016/j.dsp.2007.12.004
[4]J. G. Proakis and D.K. Manolakis, Digital Signal Processing. Pearson, 4th edition, ISBN-10: 0131873741, Apr 2007. Retrieved on-line from: https://www.pearson.com/us/higher-education/program/ Proakis-Digital-Signal-Processing-4th-Edition/PGM258227.html
[5]H. Dinh, et. al. Chirp signal formats and techniques. Qualcomm Inc, 2019 US Patent # US20190268841A1. https://patents.google.com/patent/US20190268841A1/en
[6]Li X., Bi G. Time-Frequency Filtering and Its Application in Chirp Signal Detection. In: Wu Y. (eds) Computing and Intelligent Systems. ICCIC 2011. Communications in Computer and Information Science, vol 234. Springer, Berlin, Heidelberg. https://link.springer.com/chapter/10.1007/978-3-642-24091-1_31
[7]D.J. Easterbrook. Chapter 14 - Cause of Global Climate Changes: Correlation of Global Temperature, Sunspots, Solar Irradiance, Cosmic Rays, and Radiocarbon and Berylium Production Rates, Editor(s): Don J. Easterbrook, Evidence-Based Climate Science (Second Edition), Elsevier, 2016, Pages 245-262, ISBN 9780128045886, https://doi.org/10.1016/B978-0-12-804588-6.00014-8.
[8]National Oceanic and Atmospheric Administration (NOAA), Sunspots and Solar Cycles. NOAA, Feb 2013. Retrieved on-line from: https://www.swpc.noaa.gov/phenomena/sunspotssolar-cycle
[9]Giron-Sierra J.M. Time-Frequency Analysis. In: Digital Signal Processing with Matlab Examples, Volume 1. Signals and Communication Technology. 2017. Springer, Singapore. https://doi.org/10.1007/978-981-10-2534-1_7
[10]E. Sejdić, I. Djurović, J. Jiang. Time-frequency feature representation using energy concentration: An overview of recent advances. Digital Signal Processing, vol. 19, no. 1, pp. 153–183, January 2009.
[11]P. Singh. Time-Frequency analysis via the Fourier Representation. arXiv:1604.04992, Information Theory (cs.IT); Numerical Analysis (math.NA), 2016.
[12]B. Tatsuro. Time-Frequency Analysis Using Short Time Fourier Transform. The Open Acoustics Journal. Vol.5. 2012, pages 32-38. http://10.2174/1874837601205010032.
[13]I. M. Zoukaneri and M. J. Porsani. High-resolution time frequency analysis using Wigner-Ville Distribution and the Maximum Entropy Method: Application for gas and hydrates identification. 13th International Congress of the Brazilian Geophysical Society & EXPOGEF, Rio de Janeiro, Brazil, 2013. https://doi.org/10.1190/sbgf2013-198.
[14]R. G. Osuna, Introduction to Speech Processing. This lecture notes, CSE@TAMU, 2002. Retrieved on-line from: http://research.cs.tamu.edu/prism/lectures/sp/l6.pdf
[15]Cao, J., Chen, G. Fast computation of Wigner Ville distribution. J. of Shanghai Univ. 7, 265–269. 2003. https://doi.org/10.1007/s11741-003-0036-5