Optimal GNSS Time Tracking for Long-term Stable Time Realisation in Synchronised Atomic Clocks
This work addresses the specific problem of maintaining long-term time stability in synchronized atomic clock ensembles, which is crucial for applications requiring precise timing, but it appears to be an incremental improvement on existing methods.
The authors tackled the problem of long-term accuracy deterioration in synchronized miniature atomic clocks by proposing an optimal GNSS time tracking algorithm that uses Kalman filter estimation and optimized feedback control to steer the ensemble average toward GNSS time, achieving better long-term accuracy and precision as demonstrated numerically.
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.