Instantaneous frequency estimation and localization

El-Jaroudi, A., Emresoy, M. K., Stankovic, L. J., Hussain, Z. M., Boashash, B., O'Shea, P. J., Barkat, B., Sucic, V., Lerga, J., Rankine, L. J., Djurovic, I., Simeunovic, M., Djukanovic, S. and Ristic, B. (2016). Instantaneous frequency estimation and localization. In Boualem Boashash (Ed.), Time-frequency signal analysis and processing: a comprehensive reference Second edition ed. (pp. 575-635) Amsterdam, Netherlands: Academic Press. doi:10.1016/B978-0-12-398499-9.00010-8

Author El-Jaroudi, A.
Emresoy, M. K.
Stankovic, L. J.
Hussain, Z. M.
Boashash, B.
O'Shea, P. J.
Barkat, B.
Sucic, V.
Lerga, J.
Rankine, L. J.
Djurovic, I.
Simeunovic, M.
Djukanovic, S.
Ristic, B.
Title of chapter Instantaneous frequency estimation and localization
Title of book Time-frequency signal analysis and processing: a comprehensive reference
Place of Publication Amsterdam, Netherlands
Publisher Academic Press
Publication Year 2016
Sub-type Research book chapter (original research)
DOI 10.1016/B978-0-12-398499-9.00010-8
Open Access Status Not yet assessed
Series EURASIP and Academic Press series in signal and image processing
Edition Second edition
ISBN 9780123984999
Editor Boualem Boashash
Chapter number 10
Start page 575
End page 635
Total pages 61
Total chapters 18
Collection year 2017
Language eng
Formatted Abstract/Summary
In many applications, a critical feature of a nonstationary signal is provided by its instantaneous frequency (IF), which accounts for the signal spectral variations as a function of time. This chapter presents methods and algorithms for the localization and estimation of the signal IF using timefrequency (t, f) based methods. The topic is covered in eight sections with appropriate internal crossreferencing to this and other chapters. In addition to filter banks and zero-crossings, one of the first conventional approaches for IF estimation used the spectrogram. To account for its major limitations related to accuracy, resolution, window dependence, and sensitivity, improvements were made by introducing iterative methodologies on the estimate provided by the first moment of the spectrogram (Section 10.1). Another approach uses an adaptive algorithm for IF estimation using the peak of suitable Time-Frequency Distributions (TFDs) with adaptive window length (Section 10.2). This method was extended to the case of multicomponent signals using high-resolution TFDs such as the modified B-distribution (Section 10.3). When the signals considered have polynomial FM characteristics, both the peak of the polynomial WVD and higher-order ambiguity functions can be used as IF estimators (Section 10.4). In the special case when the signals are subject to random amplitude modulation (or multiplicative noise), IF estimation procedures are described using the peak of the Wigner Ville Distribution (WVD) for linear Frequency Modulated (FM) signals, and the peak of the Polynomial Wigner Ville Distribution (PWVD) for nonlinear FM signals (Section 10.5). Then, a comparison of multicomponent IF estimation algorithms is provided (Section 10.6); and methods for instantaneous frequency and polynomial phase parameters estimation using linear time-frequency representations are presented (Section 10.7). Methods based on particle filtering and sequential Bayesian estimation are conceptually developed for multicomponent FM signals IF estimation (Section 10.8). Finally, Section 10.9 describes briefly the Viterbi algorithm for completeness.
Q-Index Code B1
Q-Index Status Provisional Code
Institutional Status UQ

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