Correlative time-frequency analysis and classification of nonstationary random processes

Author

Kozek, Werner ; Hlawatsch, Franz ; Kirchauer, Heinrich ; Trautwein, Uwe

Author_Institution

Dept. of Math., Wien Univ., Austria

fYear

1994

fDate

25-28 Oct 1994

Firstpage

417

Lastpage

420

Abstract

The expected ambiguity function (EAF) is shown to provide a generalization of stationary correlation analysis to nonstationary random processes. Important properties of the EAF are discussed, and the EAFs of special processes are considered. Based on the EAF, a fundamental classification (underspread/overspread) of nonstationary processes is introduced and shown to be relevant to time-varying spectral analysis

Keywords

Wigner distribution; correlation theory; random processes; spectral analysis; time-frequency analysis; white noise; classification; correlative time-frequency analysis; expected ambiguity function; nonstationary random processes; overspread; stationary correlation analysis; time-varying spectral analysis; underspread; white noise; Autocorrelation; Fourier transforms; Mathematics; Random processes; Signal processing; Spectral analysis; Testing; Time frequency analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Time-Frequency and Time-Scale Analysis, 1994., Proceedings of the IEEE-SP International Symposium on

Conference_Location

Philadelphia, PA

Print_ISBN

0-7803-2127-8

Type

conf

DOI

10.1109/TFSA.1994.467326

Filename

467326

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3365259