Robust complex-time distributions based on reconstruction algorithms

The complex-time distributions has been used as an important time-frequency tool for the analysis of signals characterized by the fast-varying instantaneous frequency. However, in the presence of impulse noise, the distribution performance is seriously degraded, even in the case when the robust statistics (such as L-estimation) is applied instead of standard distribution calculation. As an efficient solution, we propose a combination of compressive sensing reconstruction and time-frequency distribution, which will be referred as compressive sensing based complex-time distribution.