Contributions to Books:
F. Hlawatsch, G. Matz:
"Time-frequency methods for non-stationary statistical signal processing";
in: "Time-Frequency Analysis: Concepts and Methods",
F. Hlawatsch, F. Auger (ed.);
ISTE and Wiley,
Time-frequency (TF) methods can be used to analyze and process non-stationary random processes in an efﬁcient and intuitive manner. This chapter presents some of the non-parametric methods in this area. We ﬁrst discuss two different deﬁnitions of a "TF spectrum" for non-stationary processes. For the important subclass of underspread processes, it is demonstrated that the various TF spectra are effectively equivalent and approximately satisfy several desirable properties. Methods for estimating TF spectra are presented and studied. Finally, we discuss the use of TF spectra for processing non-stationary random processes of the underspread type. Simple formulations of quasi-optimum estimators and detectors are proposed, which generalize methods for the stationary case (such as, for instance, Wiener-type ﬁlters) to the case of underspread non-stationary random processes. These "TF estimators/detectors" have the advantage of allowing an intuitive interpretation and being numerically stable and efﬁcient.
underspread non-stationary random processes, statistical signal processing, time-varying spectra, Wigner-Ville spectrum, evolutionary spectrum, time-frequency analysis, non-stationary estimation, Wiener ﬁlter, non-stationary detection
Electronic version of the publication:
Created from the Publication Database of the Vienna University of Technology.