An extended Kalman filter for identification of biased sinusoidal signals

Abstract

This paper presents a method to address the problem of presence of a bias component in the input sinusoidal signal of the EKF frequency tracker. The bias component may be intrinsically present in the input signal or may be generated due to temporary system faults or can be generated by measurement devices. A new state space model has been developed for parameter estimation of a biased sinusoidal signal in Gaussian noise using extended Kalman filter (EKF). The proposed model not only has the ability of estimating constant parameters, but also tracks variations in the bias component and frequency. Simulation results demonstrate the desirable performance of the proposed EKF for parameter estimation of a biased sinusoidal signal.