Short‐term GNSS satellite clock stability

Radio Science ◽  
2015 ◽  
Vol 50 (8) ◽  
pp. 813-826 ◽  
Author(s):  
E. Griggs ◽  
E. R. Kursinski ◽  
D. Akos
Keyword(s):  
Satellite Clock ◽  
Short Term ◽  
Clock Stability

scholarly journals A New BDS-2 Satellite Clock Bias Prediction Algorithm with an Improved Exponential Smoothing Method

2020 ◽  
Vol 10 (21) ◽  
pp. 7456
Author(s):  
Ye Yu ◽  
Mo Huang ◽  
Changyuan Wang ◽  
Rui Hu ◽  
Tao Duan
Keyword(s):  
Forecast Accuracy ◽  
Satellite System ◽  
Exponential Smoothing ◽  
Normal Operation ◽  
Prediction Algorithm ◽  
Satellite Clock ◽  
Short Term ◽  
Error Learning ◽  
Satellite Clock Bias ◽  
New Algorithms

High-accuracy and dependable prediction of the bias of space-borne atomic clocks is extremely crucial for the normal operation of the satellites in the case of interrupted communication. Currently, the clock bias prediction for the Chinese BeiDou Navigation Satellite System (BDS) remains still a huge challenge. To develop a high-precision approach for forecasting satellite clock bias (SCB) in allusion to analyze the shortcomings of the exponential smoothing (ES) model, a modified ES model is proposed hereof, especially for BDS-2 satellites. Firstly, the basic ES models and their prediction mechanism are introduced. As the smoothing coefficient is difficult to determine, this leads to increasing fitting errors and poor forecast results. This issue is addressed by introducing a dynamic “thick near thin far (TNTF)” principle based on the sliding windows (SW) to optimize the best smoothing coefficient. Furthermore, to enhance the short-term forecasted accuracy of the ES model, the gray model (GM) is adopted to learn the fitting residuals of the ES model and combine the forecasted results of the ES model with the predicted results of the GM model from error learning (ES + GM). Compared with the single ES models, the experimental results show that the short-term forecast based on the ES + GM models is improved remarkably, especially for the combination of the three ES model and GM model (ES3 + GM). To further improve the medium-term prediction accuracy of the ES model, the new algorithms in ES with GM error learning based on the SW (ES + GM + SW) are presented. Through examples analysis, compared with the single ES2 (ES3) model, results indicate that (1) the average forecast precision of the new algorithms ES2 + GM + SW (ES3 + GM + SW) can be dramatically enhanced by 49.10% (56.40%) from 5.56 ns (6.77 ns) to 2.83 ns (2.95 ns); (2) the average forecast stability of the new algorithms ES2 + GM + SW (ES3 + GM + SW) is also observably boosted by 53.40% (49.60%) from 8.99 ns (16.13 ns) to 4.19 ns (8.13 ns). These new coupling forecast models proposed in this contribution are more effective in clock bias prediction both forecast accuracy and forecast stability.

Real-Time GPS Clock Monitoring with IGS Ultra-Rapid Products

2011 ◽  
Vol 301-303 ◽  
pp. 1293-1298
Author(s):  
Youn Jeong Heo ◽  
Jeongho Cho ◽  
Moon Beom Heo
Keyword(s):  
Real Time ◽  
Carrier Phase ◽  
Time Transfer ◽  
Satellite Clock ◽  
Short Term ◽  
Transfer Method ◽  
The Stability

The objective of this study is to develop a real-time strategy that results in higher precision than any real-time solutions currently available for GPS satellite clock monitoring. A real-time time transfer methodology was employed for satellite clock monitoring, composed of carrier phase smoothed code measurements and IGS ultra-rapid products to obtain precise satellite positions. The performance of the time transfer method was assessed by comparison with the results based on the all-in-view method using the broadcasting ephemeredes. The results showed that the stability of satellite clock monitoring for a short-term period was improved by the proposed method.

scholarly journals Clock bias prediction algorithm for navigation satellites based on a supervised learning long short-term memory neural network

GPS Solutions ◽  
2021 ◽  
Vol 25 (2) ◽  
Author(s):  
Bohua Huang ◽  
Zengxi Ji ◽  
Renjian Zhai ◽  
Changfu Xiao ◽  
Fan Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Supervised Learning ◽  
Prediction Accuracy ◽  
Short Term Memory ◽  
Satellite Clock ◽  
Short Term ◽  
Term Prediction ◽  
Satellite Clock Bias ◽  
Accuracy And Stability ◽  
Better Than

AbstractIn a satellite navigation system, high-precision prediction of satellite clock bias directly determines the accuracy of navigation, positioning, and time synchronization and is the key to realizing autonomous navigation. To further improve satellite clock bias prediction accuracy, we establish a satellite clock bias prediction model by using long short-term memory (LSTM) that can accurately express the nonlinear characteristics of the navigation satellite clock bias. Outliers in the original clock bias should be preprocessed before using the clock bias for prediction. By analyzing the working principle of the traditional median absolute deviations method, the ambiguity of the mathematical model of that method was improved. Experimental results show that the improved method is better than the traditional method at detecting gross errors. The single difference sequence of the preprocessed satellite clock bias was taken as the research object. First, a quadratic polynomial model was fit to the trend term of the single difference sequence. Second, based on the LSTM neural network model and the basic principles of supervised learning, a supervised learning LSTM network model (SL-LSTM) was proposed that models cyclic and random terms. Finally, the prediction function of the satellite clock bias was realized by extrapolating the model by adding a trend term. We adopt the GPS precision satellite clock bias of International GNSS Service data forecast experiments and apply wavelet neural network (WNN), autoregressive integrated moving average (ARIMA), and quadratic polynomial (QP) models to compare their prediction effects. The average prediction RMSE for 3 h, 6 h, 12 h, 1 d, and 3 d based on the SL-LSTM improved by approximately −21.80, −1.85, 8.57, 2.27, and 40.79%, respectively, compared with the results of the WNN. The average prediction RMSE based on the SL-LSTM improved by approximately 38.23, 65.48, 80.22, 85.18, and 94.51% compared with the ARIMA results. The average prediction RMSE based on the SL-LSTM improved by approximately 82.37, 75.88, 67.24, 45.71, and 58.22% compared with the QP results. Compared with the WNN, the SL-LSTM method has no obvious advantages in the prediction accuracy and stability in short-term prediction but achieves a better long-term prediction accuracy and stability. With an increased prediction duration, the SL-LSTM method is clearly better than the other three methods in terms of the prediction accuracy and stability. The results indicated that the quality of satellite clock bias prediction by the SL-LSTM method is better than that of the above three methods and is more suitable for the middle- and long-term prediction of satellite clock bias.

scholarly journals Characteristics and Performance Evaluation of QZSS Onboard Satellite Clocks

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5147 ◽  
Author(s):  
Wei Xie ◽  
Guanwen Huang ◽  
Bobin Cui ◽  
Pingli Li ◽  
Yu Cao ◽  
...  
Keyword(s):  
Satellite System ◽  
Frequency Drift ◽  
Asia Pacific ◽  
Asia Pacific Region ◽  
Satellite Clock ◽  
Short Term ◽  
Performance Variation ◽  
And Performance ◽  
Clock Prediction ◽  
Clock Offset

In the Global Navigation Satellite System (GNSS) community, the Quasi-Zenith Satellite System (QZSS) is an augmentation system for users in the Asia-Pacific region. However, the characteristics and performance of four QZSS satellite clocks in a long-term scale are unknown at present. However, it is crucial to the positioning, navigation and timing (PNT) services of users, especially in Asia-Pacific region. In this study, the characteristics and performance variation of four QZSS satellite clocks, which including the phase, frequency, frequency drift, fitting residuals, frequency accuracy, periodic terms, frequency stability and short-term clock prediction, are revealed in detail for the first time based on the precise satellite clock offset products of nearly 1000 days. The important contributions are as follows: (1) It is detected that the times of phase and frequency jump are 2.25 and 1.5 for every QZSS satellite clock in one year. The magnitude of the frequency drift is about 10−18. The periodic oscillation of frequency drift of J01 and J02 satellite clocks is found. The clock offset model precision of QZSS is 0.33 ns. (2) The two main periods of QZSS satellite clock are 24 and 12 hours, which is the influence of the satellite orbit; (3) The frequency stability of 100, 1000 and 10,000 s are 1.98 × 10−13, 6.59 × 10−14 and 5.39 × 10−14 for QZSS satellite clock, respectively. The visible “bump” is found at about 400 s for J02 and J03 satellite clocks. The short-term clock prediction accuracy of is 0.12 ns. This study provides a reference for the state monitoring and performance variation of the QZSS satellite clock.

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