Adaptive complex unscented Kalman filter for frequency estimation of time-varying signals

A simple and robust non-linear filter algorithm has been proposed in this study for estimating the frequency of a time-varying sinusoidal signal under high noise conditions. The real signal is first converted to an analytical signal and its complex state-space model is derived. An unscented complex Kalman filter (CUKF) is then obtained using the complex signal model and the error covariances along with the Kalman gain are updated iteratively. Also, the stability and the convergence characteristics of the proposed filter are presented for a single sinusoid embedded in noise. It has been shown that the proposed algorithm works efficiently for the estimation of abrupt changes in signal frequency under high noise conditions. To evaluate the performance of the proposed algorithm several computer simulation results of real-time and synthetic signals are presented. Further to improve the performance of the proposed filter in the presence of significant noise and distortions, the covariance matrices are tuned iteratively.