Characteristic Analysis and Short-Term Prediction of GPS/BDS Satellite Clock Correction

Zhou, Weili; Huang, Chao; Song, Shuli; Chen, Qinming; Liu, Zhimin

doi:10.1007/978-981-10-0940-2_17

Cited by 5 publications

(4 citation statements)

References 6 publications

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“…Moreover, a wavelet neural network model was employed on single-differenced clocks between subsequent epochs to deal with influences of external factors (Wang et al 2016). Modified grey model (Zheng, Lu, and Chen 2008) and autoregressive integrated moving average model (Zhou et al 2016) were also employed to improve the prediction of the GNSS clocks (Zhou et al 2016). Compared to the GNSS clocks, the LEO satellite clock behaviors were less investigated.…”

Section: Introductionmentioning

confidence: 99%

LEO satellite clock analysis and prediction for positioning applications

Wang

El‐Mowafy

2021

Geo-spatial Information Science

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The positioning service aided by low Earth orbit (LEO) mega-constellations has become a hot topic in recent years. To achieve precise positioning, accuracy of the LEO clocks is important for single-receiver users. To bridge the gap between the applicable time of the clock products and the time of positioning, the precise LEO clocks need to be predicted over a certain period depending on the sampling interval of the clock products. This study discusses the prediction errors for periods from 10 s to 1 h for two typical LEO clock types, i.e. the ultra-stable oscillator (USO) and the oven-controlled crystal oscillator (OCXO). The prediction is based on GNSSdetermined precise clock estimates, where the clock stability is related to the GNSS estimation errors, the behaviors of the oscillators themselves, the systematic effects related to the environment and the relativistic effects, and the stability of the time reference. Based on real data analysis, LEO clocks of the two different types are simulated under different conditions, and a prediction model considering the systematic effects is proposed. Compared to a simple polynomial fitting model usually applied, the proposed model can significantly reduce the prediction errors, i.e. by about 40%-70% in simulations and about 5%-30% for real data containing different miss-modeled effects. For both clock types, short-term prediction of 1 min would result in a root mean square error (RMSE) of a few centimeters when using a very stable time reference. The RMSE amounts to about 0.1 m, when a typical real-time time reference of the national center for space studies (CNES) real-time clocks was used. For longterm prediction of 1 h, the RMSE could range from below 1 m to a few meters for the USOs, depending on the complexity of the miss-modeled effects. For OCXOs, the 1 h prediction could lead to larger errors with an RMSE of about 10 m.

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Section: Introductionmentioning

confidence: 99%

LEO satellite clock analysis and prediction for positioning applications

Wang

El‐Mowafy

2021

Geo-spatial Information Science

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“…Many methods, such as polynomial, gray, autoregressive integrated moving average (ARIMA), and neural network models, can be used for clock error prediction [ 10 , 11 , 12 ]. The linear or quadratic polynomial model with periodic terms is used as the model of IGU-P products.…”

Section: Introductionmentioning

confidence: 99%

Improved Short-Term Clock Prediction Method for Real-Time Positioning

Dai

Zhao

et al. 2017

Sensors

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The application of real-time precise point positioning (PPP) requires real-time precise orbit and clock products that should be predicted within a short time to compensate for the communication delay or data gap. Unlike orbit correction, clock correction is difficult to model and predict. The widely used linear model hardly fits long periodic trends with a small data set and exhibits significant accuracy degradation in real-time prediction when a large data set is used. This study proposes a new prediction model for maintaining short-term satellite clocks to meet the high-precision requirements of real-time clocks and provide clock extrapolation without interrupting the real-time data stream. Fast Fourier transform (FFT) is used to analyze the linear prediction residuals of real-time clocks. The periodic terms obtained through FFT are adopted in the sliding window prediction to achieve a significant improvement in short-term prediction accuracy. This study also analyzes and compares the accuracy of short-term forecasts (less than 3 h) by using different length observations. Experimental results obtained from International GNSS Service (IGS) final products and our own real-time clocks show that the 3-h prediction accuracy is better than 0.85 ns. The new model can replace IGS ultra-rapid products in the application of real-time PPP. It is also found that there is a positive correlation between the prediction accuracy and the short-term stability of on-board clocks. Compared with the accuracy of the traditional linear model, the accuracy of the static PPP using the new model of the 2-h prediction clock in N, E, and U directions is improved by about 50%. Furthermore, the static PPP accuracy of 2-h clock products is better than 0.1 m. When an interruption occurs in the real-time model, the accuracy of the kinematic PPP solution using 1-h clock prediction product is better than 0.2 m, without significant accuracy degradation. This model is of practical significance because it solves the problems of interruption and delay in data broadcast in real-time clock estimation and can meet the requirements of real-time PPP.

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“…He et al [34] consider the non-linear effects by the generalized regression neural network (GRNN) and the prediction results are enhanced. Zhou, et al uses the ARIMA method to improve short-term BeiDou clock prediction [35]. However, it still far from fully understands the characteristics of BeiDou clocks.According to extensive data analysis, we find that the key to improving BeiDou clock prediction is not the non-linear effects, since there is no substantial difference between the GPS and BeiDou clock in terms of stability.…”

mentioning

confidence: 99%

mentioning

confidence: 99%

A Practical Adaptive Clock Offset Prediction Model for the Beidou-2 System

Wang

et al. 2019

Remote Sensing

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The predicted navigation satellite clock offsets are crucial to support real-time global navigation satellite system (GNSS) precise positioning applications, especially for those applications difficult to access the real-time data stream, such as the low earth orbit (LEO) autonomous precise orbit determination. Currently, the clock prediction for the Chinese BeiDou system is still challenging to meet the precise positioning requirement. The onboard clocks of BeiDou satellites are provided by different manufacturers, and the clocks’ switch events are more frequent. Considering the satellite-specified and temporal variation of the BeiDou clocks characteristics, we intend to use an adaptive model for BeiDou clock prediction. During clock prediction, we identify different models for BeiDou clocks’ characteristics, and then address the optimal model with a cross-validation procedure. The model achieving the minimum variance in the cross-validation procedure is used for the final clock prediction. We compared the prediction results of our method with two well-recognized BeiDou ultra-rapid clock products, named GBU-P and ISU-P, respectively. The comparison results indicate that the adaptive model achieves about 1-ns precision for 3-h prediction, which corresponds to 47.3% and 32.1% precision improvement compared to the GBU-P and ISU-P products, respectively. The efficiency of the predicted clocks is further validated with the precise point positioning (PPP) data processing. The results indicate that the static PPP solution precision is improved by 21.6%–30.0% compared to the current predicted clock product. The precision improvement in kinematic PPP is even more significant, which reaches 46.7%–53.9% with respect to these GBU-P and ISU-P products. Therefore, the proposed adaptive model is a practical and an efficient way to improve the BeiDou clock prediction.

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Characteristic Analysis and Short-Term Prediction of GPS/BDS Satellite Clock Correction

Cited by 5 publications

References 6 publications

LEO satellite clock analysis and prediction for positioning applications

LEO satellite clock analysis and prediction for positioning applications

Improved Short-Term Clock Prediction Method for Real-Time Positioning

A Practical Adaptive Clock Offset Prediction Model for the Beidou-2 System

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