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The weak frequency anomaly detection method of atomic clocks based on Kalman filter and extrapolation-accumulation
Author(s) -
R. Yan,
JunLiang Liu,
Jianfeng Wu,
Chao Xu,
Yonghui Hu
Publication year - 2021
Publication title -
measurement and control
Language(s) - English
Resource type - Journals
eISSN - 2051-8730
pISSN - 0020-2940
DOI - 10.1177/00202940211000073
Subject(s) - extrapolation , kalman filter , anomaly detection , outlier , statistics , algorithm , detection threshold , filter (signal processing) , ensemble kalman filter , computer science , mathematics , computational physics , physics , extended kalman filter , artificial intelligence , real time computing , computer vision
A new method based on the innovation of Kalman filter and extrapolation-accumulation is proposed to detect weak frequency anomalies in atomic clocks with short detection time and high detection probability. In this method, the detection statistics of the innovation extrapolation method in several epochs are accumulated. It avoids the influence of outliers, increases the noncentrality parameter of chi-square distribution, and realizes more effective detection of weak frequency anomalies. The simulation results show that compared with the innovation method and the innovation extrapolation method, the new method has a higher detection probability for micro frequency anomalies, and the detection time is shortened. The new method is used to analyze the real data of the cesium atomic clock, and the results are consistent with the simulation results.

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