原子钟模型和频率稳定度分析方法

Atomic Clock Models and Frequency Stability Analyses

  • 摘要: 首先给出典型的原子钟时差观测量模型,包括确定性部分(时差、频差、线性频漂和周期性波动项)、随机性部分(即原子钟噪声)和观测噪声;分析了各分量对应的Allan偏差的表达式。针对部分文献对Kalman滤波器估计原子钟状态原理描述不清晰的问题,描述了原子钟随机微分方程模型和各物理量的含义,从最优估计和低通滤波器两个角度阐述其原理。针对观测噪声过大、存在周期性波动等原因造成无法准确估计原子钟噪声强度的情况,提出了综合Kalman滤波器状态估计结果和Allan偏差图,估计原子钟噪声和观测噪声强度的方法;提出了3种不同的估计线性频漂幅度的方法,并通过实测数据相互验证;针对周期性波动在时差中不明显的问题,结合原子钟随机微分方程模型,提出了综合Kalman滤波器状态估计的结果和对数Allan偏差图估计周期性波动周期和幅度的方法。对两台国产氢钟的实测数据进行了验证,证明该方法物理原理清晰,操作简便易行,具有实用性。通过该方法可以外推得到所有平滑时间的Allan偏差估计值。

     

    Abstract: This paper describes atomic clock models and frequency stability analyses methods. The time deviation observation model is comprised of the deterministic part (the time deviation, the frequency deviation, the linear frequency drift and the periodical part), the stochastic part which is the clock noise and the observation noise. This paper gives the Allan deviation expressions of these parts. The principle of utilizing Kalman filter to estimate the clock status is not illustrated in some paper clearly. Thus, this paper describes the atomic clock stochastic differential equations and parameters in detail. Then, we propose a method to estimate the clock noises and observation noise levels by means of the Kalman filter estimation results and Allan deviation pictures. Three methods of estimating the linear frequency drift level are proposed and validated by measurements. This paper also proposes the method of estimating the period and level of the periodical part by means of the atomic clock stochastic differential equations, the Kalman filter estimation results and Allan deviation pictures. The real measurements of two domestic hydrogen masers are used to validate these methods. The principles of these methods are distinct. They are practical and easily to be realized. The methods can be used to obtain the estimated Allan deviation values at any observation interval. This research is the basis of a series of successive researches such as time scale algorithms, clock prediction algorithms, steering algorithms.

     

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