Bayesian Covariance Estimation for Kalman Filter based Digital Carrier Synchronization

摘要

Carrier synchronization in modern mass-market GNSS receivers typically relies on traditional locked loop architectures for estimating and tracking the synchronization parameters. Recently, it has been shown in the GNSS literature that that system architectures based on Kalman filtering methods may be used in place of standard locked-loop architectures, and may offer advantages over these architectures in terms of filter robustness in time-varying environmental conditions and the development of principled criterion for evaluating filter performance, among other things. One of the practical challenges involved in the use of a Kalman filtering based approach is the assumption of a well defined model of the process and measurement noise covariances, but this information is not directly available a priori and may change as channel conditions change during system operation. In this article, we propose a fully Bayesian methodology to estimate the measurement noise covariance at the same time that filtering takes place. An algorithm is proposed, which is validated through computer simulations.