A robust estimator for polynomial phase signals in non Gaussian noise using parallel unscented Kalman filters

In this paper, we address the problem of the estimation of polynomial phase signals (PPS) in “∈-contaminated” impulsive noise using Kalman filtering technique. We consider an original estimation method based on the exact non linear state space representation of the signal by using the unscented Kalman filter (UKF) instead of the classical approach which consists in the linearization of the system of equations and then applying the extended kalman filter (EKF). The observation noise's probability density function is assumed to be a sum of two-component Gaussians weighted by the probability of appearance of the impulsive and gaussian noises in the observations. We propose to use two unscented Kalman filters operating in parallel (PUKF) as an alternative to the classical methods which generally handle the impulsive noise by using either clipping or freezing procedures. Simulation results show that the PUKF is less sensitive to impulsive noise and gives better estimation of signal parameters compared to the recently proposed algorithms.