2019
DOI: 10.3390/rs11161850
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A Practical Adaptive Clock Offset Prediction Model for the Beidou-2 System

Abstract: 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 manufact… Show more

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Cited by 12 publications
(11 citation statements)
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“…The distribution of the stations is illustrated in Figure 5. All of these stations are capable of tracking BDS-2 and BDS-3 signals, and the observation data are publicly accessible on the Crustal Dynamics Data Information System (CDDIS) serves [12]. Additionally, the GBU orbit is used to investigate the potential positioning accuracy of PPP, considering the compatibility between orbit and clock products.…”
Section: Ppp Validationmentioning
confidence: 99%
See 1 more Smart Citation
“…The distribution of the stations is illustrated in Figure 5. All of these stations are capable of tracking BDS-2 and BDS-3 signals, and the observation data are publicly accessible on the Crustal Dynamics Data Information System (CDDIS) serves [12]. Additionally, the GBU orbit is used to investigate the potential positioning accuracy of PPP, considering the compatibility between orbit and clock products.…”
Section: Ppp Validationmentioning
confidence: 99%
“…Since the intrinsic physical characteristics of satellite clocks are very complex and instable, as well as varying space environment [10,11], it is more challenging to achieve the actual prediction process, and a simple linear function describing the clock offset series cannot meet the abovementioned demands. Despite such difficulties, numerous researches are carried out and many models are given for clock prediction, e.g., quadratic polynomial model (QPM), grey model (GM), spectrum analysis model (SAM), Kalman filter (KF), and neural network (NN) [12][13][14].…”
Section: Introductionmentioning
confidence: 99%
“…To further improve the prediction accuracy of SAM, different numbers of periodic term and different length of fitting period used in SAM are evaluated. The evaluated results show that the optimal number and length vary with the clock type and its in-orbit performance [25]. The remaining nonlinear system errors in SAM residuals are modeled and compensated by the Back Propagation (BP) neural network, and the accuracy of 24 h predicted clock offsets is around 4 ns [26].…”
Section: Introductionmentioning
confidence: 99%
“…The FFT is usually used to extract periodic terms from the clock offsets. For good frequency resolution, long-term clock offset series are applied for FFT, such as 60-day, 100-day, and even one year [25][26][27]. However, due to space environment changes, temperature variations, and various disturbances, the periodic noise is time-varying [28].…”
Section: Introductionmentioning
confidence: 99%
“…Some contributions focus on improving the clock prediction using artificial intelligence algorithms [24], for example, Back Propagation Neural Networks (BPNN) [25], Generalized Regression Neural Network (GRNN) [17], and Support Vector Machine (SVM) [26]. Based on these methods, feasible clock prediction results have been obtained for GPS and BDS with optimized parameters [27]. However, the computing complexity increases to some extent, which is important for real-time applications.…”
Section: Introductionmentioning
confidence: 99%