结合EKF与LSTM神经网络的授时/守时算法

A clock synchronization/calibration system combining EKF and LSTM neural networks

  • 摘要: 本文研究了一种在卫星授时下,提高授时信号的授时精度和守时能力方法,即利用晶振计数器,记录下每个秒脉冲时刻的晶振频率信息;将记录历史信息输入到扩展卡尔曼滤波器(extended Kalman filter,EKF)中进行滤波,消除卫星秒脉冲信号的随机误差,提取北斗卫星前N秒秒脉冲的累计时间t_CNk时刻的晶振频率fre(k)k时刻晶振变化速率v(k);并将经过EKF输出的历史数据作为训练集,输入到长短期记忆 (long short-term memory,LSTM)神经网络中建立预测模型;通过控制变量法进行算法参数调试,找到最适合的预测模型. 试验结果表明:授时算法输出的授时信号精度最大误差为34 ns;授时算法8 h累计误差为1.001 μs,平均误差小于0.125 μs/h. 有效地提高了系统授时和守时精度.

     

    Abstract: Research on methods to improve the timing accuracy and timekeeping capability of timing signals under satellite timing. By using a crystal oscillator counter, the crystal frequency information at the moment of each second pulse is recorded; the recorded historical information is input into an extended Kalman filter (EKF) for filtering, to eliminate the random error of the satellite second pulse signal, and extract the accumulated time of the first N seconds of the BeiDou satellite pulse t_CN, and the crystal oscillator frequency fre(k) at time k; and the crystal oscillator change rate v(k) at time k; and the historical data output by the EKF is used as the training set, input into the long short-term memory (LSTM) network to establish a prediction model; the algorithm parameters are debugged using the control variable method to find the most suitable prediction model. The experimental results show that the maximum error of the timing signal output by the timing algorithm is 34 ns; the cumulative error of the timing algorithm in 8 hours is 1.001 μs, and the average error is less than 0.125 μs/h. This effectively improves the timing and timekeeping accuracy of the system.

     

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