引用本文: 伍贻威, 牟卫华, 龚航, 朱祥维, 欧钢. 使用Kalman滤波器调整预测值的时间尺度算法[J]. 武汉大学学报 ( 信息科学版), 2016, 41(9): 1253-1258.
WU Yiwei, MOU Weihua, GONG Hang, ZHU Xiangwei, OU Gang. A Time Scale Algorithm Based on Adjusting Predictions Using Kalman Filters[J]. Geomatics and Information Science of Wuhan University, 2016, 41(9): 1253-1258.
 Citation: WU Yiwei, MOU Weihua, GONG Hang, ZHU Xiangwei, OU Gang. A Time Scale Algorithm Based on Adjusting Predictions Using Kalman Filters[J]. Geomatics and Information Science of Wuhan University, 2016, 41(9): 1253-1258.

## A Time Scale Algorithm Based on Adjusting Predictions Using Kalman Filters

• 摘要: 时间尺度算法用于综合钟组内所有的原子钟，建立一个频率稳定度更高的时间尺度，其核心环节可以概括为通过N-1组观测量（钟差）计算得到N台钟的权重和预测值。传统算法主要关注如何调整权重来提高时间尺度的稳定度，本文算法通过调整预测值来提高时间尺度的稳定度。本文算法使用Kalman滤波器对观测钟差进行状态估计，在Kalman滤波器每一次递推的过程中，调整一次预测值，通过每次实时调整预测值来建立时间尺度。理论推导和仿真实验都表明，本文建立的时间尺度滤除了频率白噪声，主要只含有频率随机游走噪声，所以具有很高的中短期稳定度。该时间尺度是一个连续、实时、可预测的时间尺度。

Abstract: The purpose of a time scale algorithm is to form a time scale with high frequency stability from an ensemble of clocks. The main component in a time scale algorithm generates weights and predictions for N clocks from the N-1 measured clock differences. Traditional algorithms mostly focus on how to set weights to improve the stability of the time scale. The algorithm we propose instead focuses on how to adjust predictions to improve stability. We use Kalman filters to estimate the states of measured clock differences. We adjust predictions with the every update of the Kalman filter, to form a time scale. Theoretical analyses and simulations both show that this algorithm filters out white frequency modulation noise, and the formed time scale mainly involves the random walk frequency modulation noise; hence, the time scale formed by our proposed algorithm has high short-term and middle-term stability. Itis also a continuous, real-time, and predictable time scale.

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