Analysis of nonlinear FM signals by pattern recognition of their time-frequency representation

The aim of this work is to propose a method for detection and parameter estimation of nonlinear FM signals, mono- or multicomponent, embedded in white Gaussian noise. The proposed approach consists in mapping the signal into the time-frequency plane hy a time-frequency distribution with reassignment, and then in applying a pattern recognition technique, like the Hough transform, to the time-frequency representation to recognize specific shapes. The advantages of this method over the conventional maximum likelihood estimator are 1) a simpler implementation, because it reduces the dimension of the search space and 2) a consistent attenuation of the interference terms between different components of a signal or between signal and noise.