Combining Adaptive Diffusion with Difference 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. However, the application of the WD is greatly limited by the existence of interference terms. The adaptive diffusion method proposed to remove the interference terms of the WD by Julien Gosme, et al. is to be invalid in the presence of interference terms generated by signals, whose distributions are interwoven together in the time-frequency plane of the WD. We combine the diffusion technique with difference method for removing these interference terms to improve the resolution and readability of the time-frequency representation of the Cohen class for detecting nonstationary signals.