Optimal Scale Invariant Wigner Spectrum Estimation of Gaussian Locally Self-Similar Processes Using Hermite Functions
Article
First Online:
- 27 Downloads
Abstract
This paper investigates the mean square error optimal estimation of scale invariant Wigner spectrum for the class of Gaussian locally self-similar processes, by the multitaper method. In this method, the spectrum is estimated as a weighted sum of scale invariant windowed spectrograms. Moreover, it is shown that the optimal multitapers are approximated by the quasi Lamperti transformation of Hermite functions, which is computationally more efficient. Finally, the performance and accuracy of the estimation is studied via simulation.
Keywords
Locally self-similar processes Scale invariant Wigner spectrum Multitaper method Hermite functions Time–frequency analysisMathematics Subject Classification (2010)
60G18 60G99References
- 1.Martin, W.: Time-frequency analysis of random signals. In Proceedings of the ICASSP 82, vol. 3, pp. 1325-1328 (1982)Google Scholar
- 2.Bayram, M., Baraniuk, R.G.: Multiple window time-frequency analysis. In: IEEE International Symposium Time-Frequency and Time-Scale Analysis, Paris, France, pp. 173–176 (1996)Google Scholar
- 3.Sayeed, A.M., Jones, D.L.: Optimal kernels for nonstationary spectral estimation. IEEE Trans. Signal Process. 43, 478–491 (1995)CrossRefGoogle Scholar
- 4.Wahlberg, P., Hansson, M.: Kernels and multiple windows for estimation of the Wigner–Ville spectrum of gaussian locally stationary processes. IEEE Trans. Signal Process. 55(1), 73–84 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
- 5.Hansson-Sandsten, M.: Optimal multitaper wigner spectrum estimation of a class of locally stationary processes using Hermite functions. EURASIP (2011). https://doi.org/10.1155/2011/980805 zbMATHGoogle Scholar
- 6.Flandrin, P., Borgnat, P., Amblard, P.O.: From stationarity to self-similarity and back: variations on the Lamperti transformation. Lecture Notes on Physics 621, pp. 88–117. Springer (2003)Google Scholar
- 7.Silverman, R.A.: Locally stationary random processes. IRE Trans. Inf. Theory 3(3), 182–187 (1957)CrossRefGoogle Scholar
- 8.Flandrin, P.: Scale-invariant wigner spectra and self-similarity. In: Torres, L., et al. (eds.) Signal Processing v: Theories and Applications, pp. 149–152. Elsevier, Amsterdam (1990)Google Scholar
- 9.Borgnat, P., Amblard, P.O., Flandrin, P.: Scale invariances and lamperti transformations for stochastic processes. J. Phys. A 38(10), 2081–2101 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
- 10.Maleki, Y., Rezakhah, S.: The scale invariant wigner spectrum estimation of Gaussian locally self-similar processes. Commun. Stat. Theory Methods (2012). https://doi.org/10.1080/03610926.2012.746987
- 11.Feichtinger, H.G., Zimmermann, G.: A Banach space of test functions for Gabor analysis. In: Feichtinger, H.G., Strohmer, T. (eds.) Gabor Analysis and Algorithms Theory and Applications, pp. 123–170. Birkhuser, Cambridge (1998)Google Scholar
- 12.Feichtinger, H.G.: On a new Segal algebra. Monatshefte fr Mathematik 92, 269–289 (1981)MathSciNetCrossRefzbMATHGoogle Scholar
- 13.Wahlberg, P.: The random Wigner distribution of Gaussian stochastic processes with covariance in \(S_0({\cal{R}}^{2d})\). J. Funct. Spaces Appl. 3(2), 163–181 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
- 14.Wahlberg, P.: Regularization of kernels for estimation of the Wigner spectrum of Gaussian stochastic processes. Probab. Math. Stat. 30(2), 369–381 (2010)MathSciNetzbMATHGoogle Scholar
- 15.Wahlberg, P.: The Wigner distribution of Gaussian weakly harmonizable stochastic processes. In: Advances and Applications, Birkhuser, Proceedings of the International Conference on Pseudo-Differential Operators and Related Topics Series Operator Theory (2006)Google Scholar
- 16.Loeve, M.: Probability Theory, 3rd edn. D. Van Nostrand Co., London (1963)Google Scholar
- 17.Picinbono, B., Bondon, P.: Second-order statistics of complex signals. IEEE Trans. Signal Process. 45(2), 411–420 (1997)CrossRefGoogle Scholar
- 18.Schreier, P.J., Scharf, L.L.: Stochastic time-frequency analysis using the analytic signal: why the complementary distribution matters. IEEE Trans. Signal Process. 51(12), 3071–3079 (2003). https://doi.org/10.1109/TSP.2003.818911 MathSciNetCrossRefzbMATHGoogle Scholar
- 19.Zagier, D.: Personal Communication (2014)Google Scholar
- 20.Cohen, L.: Time-frequency distributions—a review. Proc. IEEE 77, 941–981 (1989)CrossRefGoogle Scholar
- 21.Thomson, D.J.: Spectrum estimation and harmonic analysis. Proc. IEEE 70(9), 1055–1096 (1982)CrossRefGoogle Scholar
- 22.Reed, B., Simon, M.: Methods of Modern Mathematical Physics I. Wiley, New York (1975)zbMATHGoogle Scholar
- 23.Modarresi, N., Rezakhah, S.: Spectral analysis of multi-dimensional self-similar Markov processes. J. Phys. A Math. Theor. 43(12), 125004 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
Copyright information
© Springer Science+Business Media, LLC, part of Springer Nature 2017