Optimal GNSS Time Tracking for Long-term Stable Time Realisation in Synchronised Atomic Clocks

TL;DR

This paper introduces a novel GNSS time tracking algorithm using Kalman filtering to improve long-term accuracy and precision of synchronized atomic clocks without disrupting their synchronization.

Contribution

It proposes an optimal control method that enhances long-term stability of atomic clock ensembles in GNSS by minimizing Allan variance through a Kalman filter-based tracking control.

Findings

The algorithm improves long-term accuracy of atomic clocks in GNSS.

Numerical results demonstrate enhanced stability over extended periods.

Abstract

In this manuscript, we propose a novel optimal Global Navigation Satellite System (GNSS) time tracking algorithm to collectively steer an ensemble consisting of synchronising miniature atomic clocks towards standard GNSS time. The synchronising miniature atomic clocks generate a common synchronised time which has good short term performance but its accuracy and precision, which is measured by Allan variance, deteriorates in the long run. So, a supervisor designs and periodically broadcasts the proposed GNSS time tracking control to the ensemble miniature atomic clocks that steer the average of ensemble towards the average of GNSS receivers, which are receivers of GNSS time. The tracking control is constructed using a Kalman filter estimation process that estimates the difference in average of GNSS receivers and average of ensemble clocks by using relative clock readings between GNSS receivers and their adjacent ensemble clock. Under the influence of the periodically received tracking control, the stabilised ensemble clocks have better long term accuracy and precision over long averaging periods. Since the tracking control is designed to solely influence the average of the ensemble, the tracking process does not interfere with the synchronisation process and vice versa. The feedback matrix associated with the tracking control is obtained from an optimisation problem that minimises steady-state Allan variance. Numerical results are provided to show the efficacy of the proposed algorithm for enhancing long term performance.