Detecting an atomic clock frequency anomaly using an adaptive Kalman filter algorithm
文献类型：期刊论文
| 作者 | Song,Huijie1,2,3; Dong,Shaowu1,2,4; Wu,Wenjun1,2; Jiang,Meng1,3; Wang,Weixiong1,3 |
| 刊名 | Metrologia
 |
| 出版日期 | 2018-04-13 |
| 卷号 | 55期号:3 |
| 关键词 | atomic clock Kalman filter frequency anomaly adaptive factor chi-square statistics |
| ISSN号 | 0026-1394 |
| DOI | 10.1088/1681-7575/aab66d |
| 英文摘要 | Abstract The abnormal frequencies of an atomic clock mainly include frequency jump and frequency drift jump. Atomic clock frequency anomaly detection is a key technique in time-keeping. The Kalman filter algorithm, as a linear optimal algorithm, has been widely used in real-time detection for abnormal frequency. In order to obtain an optimal state estimation, the observation model and dynamic model of the Kalman filter algorithm should satisfy Gaussian white noise conditions. The detection performance is degraded if anomalies affect the observation model or dynamic model. The idea of the adaptive Kalman filter algorithm, applied to clock frequency anomaly detection, uses the residuals given by the prediction for building ‘an adaptive factor’; the prediction state covariance matrix is real-time corrected by the adaptive factor. The results show that the model error is reduced and the detection performance is improved. The effectiveness of the algorithm is verified by the frequency jump simulation, the frequency drift jump simulation and the measured data of the atomic clock by using the chi-square test. |
| 语种 | 英语 |
| WOS记录号 | IOP:0026-1394-55-3-AAB66D |
| 出版者 | IOP Publishing |
| 源URL | [http://210.72.145.45/handle/361003/10698]  |
| 专题 | 中国科学院国家授时中心 |
| 作者单位 | 1.National Time Service Center, Chinese Academy of Sciences, Xi’an 710600, People’s Republic of China 2.Key Laboratory of Time and Frequency Primary Standards, Chinese Academy of Sciences, Xi’an 710600, People’s Republic of China 3.University of Chinese Academy of Sciences, Beijing 100049, People’s Republic of China 4.School of Astronomy and Space Sciences, University of Chinese Academy of Sciences, Beijing 100049, People’s Republic of China |
| 推荐引用方式 GB/T 7714 | Song,Huijie,Dong,Shaowu,Wu,Wenjun,et al. Detecting an atomic clock frequency anomaly using an adaptive Kalman filter algorithm[J]. Metrologia,2018,55(3). |
| APA | Song,Huijie,Dong,Shaowu,Wu,Wenjun,Jiang,Meng,&Wang,Weixiong.(2018).Detecting an atomic clock frequency anomaly using an adaptive Kalman filter algorithm.Metrologia,55(3). |
| MLA | Song,Huijie,et al."Detecting an atomic clock frequency anomaly using an adaptive Kalman filter algorithm".Metrologia 55.3(2018). |
入库方式： OAI收割
来源：国家授时中心
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