Single-tone frequency tracking using a particle filter with improvement strategies

This paper investigates a robust method for extracting frequency online from a noisy sinusoidal signal. A nearly constant frequency (NCF) model, which is adapted from the target tracking discipline, is presented to describe the evolution of the time varying frequency. A particular particle filtering algorithm, called bootstrap filter, is improved with a Gaussian kernel based regularization and a Metropolis-Hastings based Markov Chain Monte Carlo (MCMC) technique, for solving this problem. Some representative scenarios are designed for tests. The results of the simulation using synthetic data show the proposed methodpsilas efficiency and superiority to some existing methods.