Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Extended Kalman Filter for oversampled dynamical phase offset estimation
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Posterior Cramer-Rao bounds for discrete-time nonlinear filtering
IEEE Transactions on Signal Processing
Design of an extended Kalman filter frequency tracker
IEEE Transactions on Signal Processing
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Analytic and Asymptotic Analysis of Bayesian CramÉr–Rao Bound for Dynamical Phase Offset Estimation
IEEE Transactions on Signal Processing
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This paper deals with the on-line carrier phase estimation in a digital receiver. We consider a Brownian phase evolution in a data aided scenario. The proposed study uses an oversampled signal model after matched filtering, leading to a coloured reception noise and a non-stationary power signal. The contribution of this paper is twofold. First, we derive the Bayesian Cramer-Rao bound for this estimation problem. Then, based on a state-space model formulation of the problem, we propose an extended Kalman filter to approach this lower bound for a BOC shaping pulse. Our numerical results illustrate the gain resulting from the use of an oversampled version of the received signal to estimate the phase offset, obtaining better performances than using a classical synchronizer.