Extended Kalman Filter frequency tracker for nonstationary harmonic signals

In this paper, we propose a frequency tracking algorithm based on an Extended Kalman Filter (EKF). We introduce a generalized state space model to estimate and track frequency of a harmonic signal embedded in broad-band noise. Such nonstationary noisy harmonic signals are characterized by time-varying frequencies and amplitudes. Developing a modified state-space model, we improve performance of EKF frequency tracker for these signals. The proposed method is also used in an adaptive algorithm to estimate noise variance which is assumed to be unknown. Simulation results reveal superiority of the proposed method compared with typical EKF, short time Fourier transform, and interpolated discrete Fourier transform.