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Open Access

A New Approach for Estimation of Instantaneous Mean Frequency of a Time-Varying Signal

EURASIP Journal on Advances in Signal Processing20052005:126031

https://doi.org/10.1155/ASP.2005.2848

Received: 1 June 2004

Published: 23 October 2005

Abstract

Analysis of nonstationary signals is a challenging task. True nonstationary signal analysis involves monitoring the frequency changes of the signal over time (i.e., monitoring the instantaneous frequency (IF) changes). The IF of a signal is traditionally obtained by taking the first derivative of the phase of the signal with respect to time. This poses some difficulties because the derivative of the phase of the signal may take negative values thus misleading the interpretation of instantaneous frequency. In this paper, a novel approach to extract the IF from its adaptive time-frequency distribution is proposed. The adaptive time-frequency distribution of a signal is obtained by decomposing the signal into components with good time-frequency localization and by combining the Wigner distribution of the components. The adaptive time-frequency distribution thus obtained is free of cross-terms and is a positive time-frequency distribution but it does not satisfy the marginal properties. The marginal properties are achieved by applying the minimum cross-entropy optimization to the time-frequency distribution. Then, IF may be obtained as the first central moment of this adaptive time-frequency distribution. The proposed method of IF estimation is very powerful for applications with low SNR. A set of real-world and synthetic signals of known IF dynamics is tested with the proposed method as well as with other common time-frequency distributions. The simulation shows that the method successfully extracted the IF of the signals.

Keywords and phrases

instantaneous frequencynonstationary signalspositive time-frequency distributionsmatching pursuitminimum cross-entropy optimizationaverage frequency

Authors’ Affiliations

(1)
Department of Electrical and Computer Engineering, Ryerson University, Toronto, Canada

Copyright

© Krishnan 2005

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