Application of Parameterized Time-Frequency Analysis on Multicomponent Frequency Modulated Signals

Parameterized time-frequency (TF) transforms, with signal-dependent kernel parameters, have been proposed to analyze multicomponent frequency modulated (FM) signals. Usually, the kernel parameters are estimated through recursive approximation of TF representation (TFR) ridge when instantaneous frequency models of the components have the same parameter settings. However, it will be inapplicable if the components have the different FM sources. In this paper, we introduce a novel method that enables the parameterized TF transform to generate the well-concentrated TFR for both the monocomponent signal and a wide class of multicomponent FM signals, whose components are modulated by either the same or the different sources. The proposed method contains two aspects: 1) estimating kernel parameters based on spectrum concentration index and 2) separating components and assembling the parameterized TFRs of the separated components. An advantage of the proposed method is that it avoids the dependence of the TFR while estimating the parameters. Moreover, it is effective at low signal-to-noise rate. The validity and practical utility of the proposed method are demonstrated by both the simulated and real signals. The results show that it outperforms the traditional TF methods in providing the TFR of the improved concentration for various multicomponent FM signals.