Analysis of polynomial FM signals corrupted by heavy-tailed noise
Signal Processing
Estimation of FM signal parameters in impulse noise environments
Signal Processing
A new approach for estimation of instantaneous mean frequency of a time-varying signal
EURASIP Journal on Applied Signal Processing
Time--frequency feature representation using energy concentration: An overview of recent advances
Digital Signal Processing
International Journal of Computer Mathematics
Robust time-frequency distributions with complex-lag argument
EURASIP Journal on Advances in Signal Processing - Special issue on robust processing of nonstationary signals
LFM signal detection using LPP-Hough transform
Signal Processing
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The Wigner distribution (WD) produces highly concentrated time-frequency (TF) representation of nonstationary signals. It may be used as an efficient signal analysis tool, including the cases of frequency modulated signals corrupted with the Gaussian noise. In some applications, a significant amount of impulse noise is present. Then, the WD fails to produce satisfactory results. The robust periodogram has been introduced for spectral estimation of this kind of noisy signals. It can produce good concentration for pure harmonic signals. However, it is not so efficient in the cases of signals with rapidly varying frequency. This is the motivation for introducing the robust WD. It is a reliable TF representation tool for wide class of nonstationary signals corrupted with impulse noise. This distribution produces good accuracy of the instantaneous frequency (IF) estimation. Using the Huber (1981) loss function, a generalization of the WD is presented. It includes both the standard and the robust WD as special cases. This distribution can be used for TF analysis of signals corrupted with a mixture of impulse and Gaussian noise. The presented theory is illustrated on examples, including applications on the IF estimation and time-varying filtering of signals corrupted with a mixture of the Gaussian and impulse noise. The case study analysis of the IF estimators' accuracy, based on the standard and the robust WD forms, is performed. In order to improve the IF estimation, a median filter is applied on the obtained IF estimate