摘要
钟差预报是时间保持工作中的一项关键技术。Kalman算法作为一种最优预报算法,具有实时性的特点,在时间保持工作中得到了广泛的应用。但是由于经典Kalman算法需要准确确定模型随机误差和测量误差,否则状态估计会引入一定的误差,在原子时算法中表现为原子钟噪声和钟差测量噪声。原子钟的噪声参数值通常是通过Allan方差估计,若估计不够准确,Kalman预报将会出现误差。通过研究基于Sage窗的自适应Kalman预报算法,实时修正状态模型误差。利用自适应因子调整状态预测协方差阵有效降低了模型误差,提高了预报精度,最后通过两台氢原子钟和两台铯原子钟的实测数据验证了算法的有效性。
Clock difference prediction is a key technology in time keeping work. Kalman algorithm, as a kind of optimal prediction algorithm, has the characteristic of real-time, and is widely used in time keeping work. The classical Kalman algorithm needs to accurately know the random error of the model and measurement error; otherwise, the state estimation will bring error, which is characterized by atomic clock noise and clock difference measurement noise in the atomic time algorithm. The noise parameters of atomic clock is usually estimated through Allan variance, if the estimation is not accurate, the Kalman filter prediction error will appear. In this paper, the adaptive Kalman filtering prediction algorithm based on Sage window is studied and the state model error is corrected in real- time. The model error is reduced effectively by using adaptive factor to adjust the state prediction covariance matrix, which improves the prediction accuracy. Finally, the actually measured data of two hydrogen atomic clocks and two cesium atomic clocks verify the effectiveness of the proposed algorithm.
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2017年第7期1809-1816,共8页
Chinese Journal of Scientific Instrument
基金
国家自然科学基金(11473029)项目资助
