Resilient timekeeping algorithm with multi-observation fusion Kalman filter

Abstract

The timescales incorporated into the Primary Frequency Standard (PFS) exhibit excellent stability and accuracy. However, during the dead time of PFS, the reliability of the timescale can be compromised. To address this issue, a resilient timekeeping algorithm with a Multi-observation Fusion Kalman Filter (MFKF) is proposed. This algorithm fuses the frequency measurements from hydrogen masers with various reference frequency standards, including PFS and commercial cesium beam atomic clocks. The simulation results show that the time deviation and instability of the timescale generated by MFKF are improved compared to those with Kalman filtering. The experimental results demonstrate that even within 70 days of PFS dead time the resilient timescale generated by MFKF can operate reliably. Furthermore, it is theoretically proven that MFKF produces a smaller post-covariance than that with single-observation Kalman filtering.

Resilient evaluation algorithm of caesium fountain clock based on robust adaptive observation model

“…The measurement noise ( ) R t should be preset. Since the daily stability of the CSF1_SIM is 2.08E-15, its intensity of WFM can be estimated from equation (15), which is = ´-( ) q 6.11 10 .…”

“…The experiment selected evaluation data of the hydrogen maser HM3 by caesium fountain clock CsF1 [7,8,11,15] between MJD58239 and 58453. Throughout the 215-day period, there were 180 days of evaluation data for the CsF1, while the remaining 35 days experienced an interruption in its operation.…”

“…Incorporating a cesium fountain clock to generate time scales [7][8][9][10], or using CSF to evaluate time scales and clocks [11], can make time scales have better performance. However, CSF are laboratory products and usually cannot operate normally for a long time like commercial clocks [12][13][14][15]. Common abnormal operating situations of CSF include data interruptions, outliers, and an increase in noise levels.…”