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Definition of sampled signal spectrum and shannon’s proof of reconstruction formula

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EN
Abstrakty
EN
The objective of this paper is to show from another perspective that the definition of the spectrum of a sampled signal, which is used at present by researchers and engineers, is nothing else than an arbitrary choice for what is possibly not uniquely definable. To this end and for illustration, the Shannon’s proof of reconstruction formula is used. As we know, an auxiliary mathematical entity is constructed in this proof by performing periodization of the spectrum of an analog, bandlimited, energy signal. Admittedly, this entity is not called there a spectrum of the sampled signal - there is simply no need for this in the proof – but as such it is used in signal processing. And, it is not clear why just this auxiliary mathematical object has been chosen in signal processing to play a role of a definition of the spectrum of a sampled signal. We show here what are the interpretation inconsistences associated with the above choice. Finally, we propose another, simpler and more useful definition of the spectrum of a sampled signal, for the cases where it can be needed.
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  • Gdynia Maritime University, Gdynia, Poland
Bibliografia
  • 1. Marks II R. J. 1991. Introduction to Shannon Sampling and Interpolation Theory, Springer-Verlag, New York. - doi:10.1007/978-1-4613-9708-3.
  • 2. Vetterli M., Kovacevic J., Goyal V. K. 2014. Foundations of Signal Processing, Cambridge University Press, Cambridge. - doi:10.1017/CBO9781139839099.
  • 3. Oppenheim A. V., Schafer R. W., Buck J. R. 1998. Discrete-Time Signal Processing, Prentice Hall, New Jersey.
  • 4. Bracewell R. N. 2000. The Fourier Transform and Its Applications, McGraw-Hill, New York.
  • 5. McClellan J. H., Schafer R., Yoder M. 2015. DSP First, Pearson, London, England.
  • 6. Brigola R. 2013. Fourier-Analysis und Distributionen, edition swk, Hamburg.
  • 7. So H. C. 2019. Signals and Systems - Lecture 6 - Discrete-Time Fourier Transform, www.ee.cityu.edu.hk/~hcso/ee 3210.html, accessed December 2019.
  • 8. Wang R. 2010. Introduction to Orthogonal Transforms with Applications in Data Processing and Analysis, Cambridge University Press, Cambridge, England.
  • 9. Ingle V. K., Proakis J. G. 2012. Digital Signal Processing Using Matlab, Cengage Learning, Stamford, CT, USA.
  • 10. Jenkins W. K. 2009. Fourier Methods for Signal Analysis and Processing in Chen W. K. 2009. Fundamentals of Circuits and Filters, CRC Press, Boca Raton, FL, USA. - doi:10.1201/9781420046076-c1.
  • 11. Schwartz L. 1950-1951. Théorie des distributions, Hermann, Paris, France.
  • 12. Dirac P. A. M. 1947. The Principles of Quantum Mechanics, University Press, Oxford, England.
  • 13. Borys A. 2020a. Spectrum aliasing does occur only in case of non-ideal signal sampling, IEEE Transactions on Information Theory, submitted for publication.
  • 14. Borys A. 2020b. Extended definition of spectrum of sampled signal via multiplying this signal by Dirac deltas or using reconstruction formula, IEEE Transactions on Circuits and Systems Part II: Express Briefs, submitted for publication.
  • 15. Shannon C. E. 1949. Communications in the presence of noise, Proceedings of the IRE, vol. 37, 10–21. - doi:10.1109/JRPROC.1949.232969.
  • 16. Oppenheim A. V., Willsky S., Nawab S. H. 1996. Signals and Systems, Prentice Hall, Upper Saddle River, USA.
  • 17. Borys A. 2020c. On derivation of discrete time Fourier transform from its continuous counterpart, Intl Journal of Electronics and Telecommunications, vol. 66, no. 2, 355-368.
  • 18. Osgood B. 2014. The Fourier Transform and its Applications, Lecture Notes EE261, Stanford University Stanford, USA.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-bf295ae8-c768-40cc-bfa3-cd36035c477f
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