Optimal kernel design of smooth-windowed wigner-ville distribution for digital communication signal

Abstract

Bilinear time-frequency analysis has been widely used to analyze time-varying signals such as in speech, music and other acoustical signals, sonar, radar, geophysics and biological signals. However, a major drawback of this method is the presence of cross-terms in the time-frequency representations (TFR’s) [1]. These terms, if not removed, will reduce the auto-terms resolution and make interpretation of the true signal characteristics difficult [2]. To overcome this, most of the TFD’s employ some kind of smoothing kernel, window, or filter [3]. Smoothing however, causes the autoterms to be smeared and as a result, the TFR losses its concentration [4]. For signal analysis and classification, an optimal distribution should have reasonable cross-terms suppression and minimal smearing of the auto-terms. Previous works have shown that the optimal kernel is signal-dependant [2,3,5]. Generally, there is no known practical fixed kernel TFD which would perform well for all signals. A kernel might perform very well for a certain class of signal but is not optimal for other type of signals. Most of the researches in optimal kernel design focus mainly on linear FM [2,3,5,6] and biological signals [7,8]. Not much attention has been given to digital communication signals.