Signal content estimation based on the short-term time-frequency Rényi entropy of the S-method time-frequency distribution
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A key characteristic of a nonstationary signal, when analyzed in the time-frequency domain, is the signal complexity, quantified as the number of components in the signal. This paper describes a method for the estimation of this number of components of a signal using the short-term Rényi entropy of its time-frequency distribution (TFD). We focus on the characteristics of TFDs that make them suitable for such a task. The performance of the proposed algorithm is studied with respect to the parameters of the S-method TFD, which combines the virtues of both the spectrogram and the Wigner-Ville distribution. Once the optimal parameters of the TFD have been determined, the applicability of the method in the analysis of signals in low SNRs and real life signals is assessed
- Electrical Engineering [2383 items ]
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Estimating the number of components of a multicomponent nonstationary signal using the short-term time-frequency Rényi entropy Sucic, Victor; Saulig, Nicoletta; Boashash, Boualem ( Springer , 2011 , Article)The time-frequency Rényi entropy provides a measure of complexity of a nonstationary multicomponent signal in the time-frequency plane. When the complexity of a signal corresponds to the number of its components, then this ...
An automatic time-frequency procedure for interference suppression by exploiting their geometrical features Saulig, Nicoletta; Sucic, Victor; Boashash, Boualem ( IEEE , 2011 , Conference Paper)This paper presents an adaptive method for interference suppression in the Wigner-Ville distribution. The structure of the artifacts in the Wigner-Ville distribution has been analyzed to optimally mask the signal Wigner-Ville ...
Boashash B.; Khan N.A.; Ben-Jabeur T. ( Elsevier Inc. , 2015 , Article)This paper presents a tutorial review of recent advances in the field of time-frequency (t, f) signal processing with focus on exploiting (t, f) image feature information using pattern recognition techniques for detection ...