Robust estimation of high-order phase dynamics using Variational Bayes inference

Cycle slips strongly impact the performance of phase tracking algorithm leading, in the worst case, to a permanent loss of lock of the signal. In this paper, we propose a new nonlinear phase estimator to obtain more robust tracks. The latter stems from a Variational Bayes (VB) approximation used within the optimal Bayesian filtering formulation in case of high-order phase dynamics. A comparison with a more conventional technique, namely a Kalman filter based PLL (Phase Lock Loop), is performed in terms of mean square error of the phase estimate and mean time to first slip. Results show that the proposed method outperforms the conventional linear filter with respect to both metrics, especially at low signal-to-noise ratio.