Comparison of WVD based time-frequency distributions

Abstract

Time Frequency distributions (TFD) are two-dimensional functions that indicate the time-varying frequency content of one-dimensional signals. The lack of a single distribution that is “best “ for all applications has resulted in a proliferation of TFDs, each corresponding to a different, fixed mapping from signals to the time-frequency plane. A major drawback of all fixed mappings is that, for each mapping, the resulting time frequency representation is satisfactory only for a limited class of signals. This drawback has been overcome by using a signal dependent TFD using a radially Gaussian kernel which has been formulated in Cohen's class as an optimisation problem. In this paper, we compare the different fixed kernel TFDs of Cohen's class-Wigner-Ville, Choi-Williams, Zhao-Atlas-Marks and Born-Jordan distributions with the signal dependent TFD by applying them to multi-component signals.