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http://dx.doi.org/10.11003/JPNT.2021.10.3.197

Comparison of AT1- and Kalman Filter-Based Ensemble Time Scale Algorithms  

Lee, Ho Seong (Time and Frequency Group, Korea Research Institute of Standards and Science)
Kwon, Taeg Yong (Time and Frequency Group, Korea Research Institute of Standards and Science)
Lee, Young Kyu (Time and Frequency Group, Korea Research Institute of Standards and Science)
Yang, Sung-hoon (Time and Frequency Group, Korea Research Institute of Standards and Science)
Yu, Dai-Hyuk (Time and Frequency Group, Korea Research Institute of Standards and Science)
Park, Sang Eon (Time and Frequency Group, Korea Research Institute of Standards and Science)
Heo, Myoung-Sun (Time and Frequency Group, Korea Research Institute of Standards and Science)
Publication Information
Journal of Positioning, Navigation, and Timing / v.10, no.3, 2021 , pp. 197-206 More about this Journal
Abstract
We compared two typical ensemble time scale algorithms; AT1 and Kalman filter. Four commercial atomic clocks composed of two hydrogen masers and two cesium atomic clocks provided measurement data to the algorithms. The allocation of relative weights to the clocks is important to generate a stable ensemble time. A 30 day-average-weight model, which was obtained from the average Allan variance of each clock, was applied to the AT1 algorithm. For the reduced Kalman filter (Kred) algorithm, we gave the same weights to the two hydrogen masers. We also compared the frequency stabilities of the outcome from the algorithms when the frequency offsets and/or the frequency drift offsets estimated by the algorithms were corrected or not corrected by the KRISS-made primary frequency standard, KRISS-F1. We found that the Kred algorithm is more effective to generate a stable ensemble time scale in the long-term, and the algorithm also generates much enhanced short-term stability when the frequency offset is used for the calculation of the Allan deviation instead of the phase offset.
Keywords
time scale; algorithms; ensemble clock; AT1; Kalman filter;
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