Quantifying features in the dynamic spectra of radio pulsars: localization of fringe features using wavelet transforms

Details Quantifying features in the dynamic spectra of radio pulsars: localization of fringe features using wavelet transforms Quantifying features in the dynamic spectra of radio pulsars: localization of fringe features using wavelet transforms

Nov 1993

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1993spie.2034..414f&link_type=abstract

Proc. SPIE Vol. 2034, p. 414-425, Wavelet Applications in Signal and Image Processing, Andrew F. Laine; Ed.

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Wavelet analysis offers a technique for signal detection particularly suited for non-stationary signals. Other methods of signal analysis, namely Fourier techniques, make explicit assumptions about the signals to be analyzed. If these assumptions hold, then the results can be striking. Conversely, if the signal does not ideally match the properties assumed by the analysis function then the results can be indecisive or ambiguous. The standard Fourier transform assumes a basis function of sines and cosines underlies all signals and analyzes the fluctuation frequency of the signal function in equal bandwidth units. Wavelet theory offers a more general technique for solving a wide variety of signal processing problems using an octave bandwidth approach. The Wavelet transform offers an alternative method for analyzing functions that have variable periodicity over the duration of a data set, either through localization or a change in the oscillation period. The Wavelet transform is shown to be a tool for the analysis of general signals. Astrophysical data sets often provide interesting applications of signal processing techniques. In this paper the Morlet Wavelet transform is applied to the preliminary analysis of radio pulsar dynamic spectra.

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