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 issu...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 singleobservation Kalman filtering.展开更多
基金This work was supported by the National Natural Science Foundation of China(Grant No.41931076)the National Key Research and Development Program of China(grant number 2020YFB0505801).
文摘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 singleobservation Kalman filtering.
