Generalized Morse wavelets

This paper examines the class of generalized Morse wavelets, which are eigenfunction wavelets suitable for use in time-varying spectrum estimation via averaging of time-scale eigenscalograms. Generalized Morse wavelets of order k (the corresponding eigenvalue order) depend on a doublet of parameters (/spl beta/, /spl gamma/); we extend results derived for the special case /spl beta/ = /spl gamma/ = 1 and include a proof of "the resolution of identity." The wavelets are easy to compute using the discrete Fourier transform (DFT) and, for (/spl beta/, /spl gamma/) = (2m, 2), can be computed exactly. A correction of a previously published eigenvalue formula is given. This shows that for /spl gamma/ > 1, generalized Morse wavelets can outperform the Hermites in energy concentration, contrary to a conclusion based on the /spl gamma/ = 1 case. For complex signals, scalogram analyses must be carried out using both the analytic and anti-analytic complex wavelets or odd and even real wavelets, whereas for real signals, the analytic complex wavelet is sufficient.

[1]  Dr. M. G. Worster Methods of Mathematical Physics , 1947, Nature.

[2]  Stephen A. Dyer,et al.  Digital signal processing , 2018, 8th International Multitopic Conference, 2004. Proceedings of INMIC 2004..

[3]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

[4]  D. Thomson,et al.  Spectrum estimation and harmonic analysis , 1982, Proceedings of the IEEE.

[5]  Richard G. Baraniuk,et al.  Multiple Window Time Varying Spectrum Estimation , 2000 .

[6]  Richard G. Baraniuk,et al.  Multiple window time-frequency analysis , 1996, Proceedings of Third International Symposium on Time-Frequency and Time-Scale Analysis (TFTS-96).

[7]  Ingrid Daubechies,et al.  Time-frequency localization operators: A geometric phase space approach , 1988, IEEE Trans. Inf. Theory.

[8]  T. W. Parks,et al.  Time-frequency concentrated basis functions , 1990, International Conference on Acoustics, Speech, and Signal Processing.

[9]  A. T. Walden,et al.  Polarization phase relationships via multiple Morse wavelets. I. Fundamentals , 2003, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[10]  Patrick Flandrin,et al.  Time-Frequency/Time-Scale Analysis , 1998 .

[11]  Boualem Boashash,et al.  Multiple window spectrogram and time-frequency distributions , 1994, Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing.

[12]  I. Daubechies,et al.  Time-frequency localisation operators-a geometric phase space approach: II. The use of dilations , 1988 .

[13]  Matthias Holschneider,et al.  Wavelets - an analysis tool , 1995, Oxford mathematical monographs.

[14]  Sinisa Pajevic,et al.  Wavelets: An analysis tool , 1997 .

[15]  Patrick Flandrin,et al.  Maximum signal energy concentration in a time-frequency domain , 1988, ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing.

[16]  I. S. Gradshteyn Table of Integrals, Series and Products, Corrected and Enlarged Edition , 1980 .

[17]  M. Reed Methods of Modern Mathematical Physics. I: Functional Analysis , 1972 .