An Improvement on Adaptive Diffusion for Detecting Nonstationary Signals

Time-frequency analysis aims to construct a density function of time and frequency to reveal the frequency components in signals to be analyzed and the evolution of the frequency of signals with time. The Wigner distribution (WD) is one of the most fundamental and widely used methods for analyzing nonstationary signals in the fields of radar, communication, etc. Despite its remarkable properties, the application of the WD is greatly limited by the existence of interference terms. An adaptive diffusion method originally used in image processing was proposed to remove the interference terms of the WD by Julien Gosme, Cedric Richard and Paulo Goncalves in Nov 2005. This method is to be invalid in the presence of very strong interference terms generated by signals whose distributions are interweaved together in the time-frequency plane of the WD. We combine the diffusion technique with difference method for removing these interference terms to efficiently improve the resolution and readability of the time-frequency representation of the Cohen class