Instantaneous Frequency Tracking under Model Uncertainty via Dynamic Model Averaging and Particle Filtering

We consider a state-space modeling approach for online estimation of a signal's instantaneous frequency (IF). We take into account the general case wherein the IF may vary over time irregularly and, therefore, given several plausible models, there is uncertainty which is the best model to use. We address this dynamical model uncertainty problem using a strategy called dynamic model averaging (DMA), in which a set of state-space models is combined with a Markov chain model for the correct model. The model transition process is specified in terms of forgetting, leading to a highly parsimonious representation. We provide six candidate models, each representing a specific evolution law of the IF. A novel particle filtering algorithm is proposed to implement this DMA strategy. Simulation results show that our method can yield accurate IF estimates in challenging settings, where the IF structure embedded in the signal changes abruptly and irregularly in a low signal-noise-ratio environment.