A Multiscale Energy-Based Time-Domain Approach for Interference Detection in Non-stationary Signals

Identification and extraction of individual modes in non-stationary multicomponent signals is a challenging task which is shared by several applications, like micro-doppler human gait analysis, surveillance or medical data analysis. State-of-the-art methods are not capable yet to correctly estimate individual modes if their instantaneous frequencies laws are not separable. The knowledge of time instants where modes interference occurs could represent a useful information to use in separation strategies. To this aim, a novel time-domain method that is capable of locating interferences is investigated in this paper. Its main property is the use of multiscale energy for selecting the best analysis scale without requiring either the use of time-frequency representations or imaging methods. The performance of the proposed method is evaluated through several numerical simulations and comparative studies with the state of the art Rényi entropy based method. Finally, an example concerning a potential application to simulated micro-doppler human gait data is provided.