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

Xu, Beizhen; Wang, Lei; Fu, Wenli; Chen, Ruizhi; Li, Tao; Zhang, Xinxin

doi:10.3390/rs11161850

Cited by 12 publications

(8 citation statements)

References 40 publications

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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%

“…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%

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Improving Clock Prediction Algorithm for BDS-2/3 Satellites Based on LS-SVM Method

Zhou

Liu

et al. 2019

Remote Sensing

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The satellite clock prediction is crucial to support real-time global satellite precise positioning services. Currently, the clock prediction for the Chinese BeiDou navigation satellite system (BDS) is still challenging to satisfy the precise positioning applications. Based on the exploration of existing prediction models, an improved model combing the spectrum analysis model (SAM) and the least-squares support-vector machine (LS-SVM) is proposed especially for BDS-2/3 satellites. Considering satellite-specific characteristics, the parameters of the LS-SVM method are optimized satellite by satellite, including input length, regularization and kernel parameters. The improved model is evaluated by comparing the predicted clocks of existing methods and the improved model. The bias of the predicted clock offsets are within ±1.0 ns for most medium Earth orbits (MEOs) over three hours employing the improved model, which is better than that of the existing methods and can be applied for several real-time precise positioning applications. The predicted clock offsets are further evaluated by applying clock corrections to precise point positioning (PPP) in both static and kinematic modes for 10 international GNSS service (IGS) Multi-GNSS Experiment (MGEX) stations, including five stations in the Asia-Pacific region. According to the practical engineering experience, 2 dm and 5 dm are defined for static and kinematic PPP, respectively, as a convergence threshold. Then, in the static PPP, the improved model is demonstrated to be effective, and positioning accuracies of some stations obtain more than 15% improvements on average for each direction, which enables them to get sub-decimeter positioning, especially in the Asia-Pacific region. In the kinematic PPP, the improved model performs much better than the others in terms of both the convergence time and the positioning accuracy. The convergence time can be shortened from 1.0 h to below 0.5 h, while the positioning accuracies are enhanced by 16.3%, 10.8%, and 18.9% on average in east, north, and up direction, respectively.

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Section: Ppp Validationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improving Clock Prediction Algorithm for BDS-2/3 Satellites Based on LS-SVM Method

Zhou

Liu

et al. 2019

Remote Sensing

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“…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%

BDS Satellite Clock Prediction Considering Periodic Variations

Zhao

et al. 2021

Remote Sensing

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The periodic noise exists in BeiDou navigation satellite system (BDS) clock offsets. As a commonly used satellite clock prediction model, the spectral analysis model (SAM) typically detects and identifies the periodic terms by the Fast Fourier transform (FFT) according to long-term clock offset series. The FFT makes an aggregate assessment in frequency domain but cannot characterize the periodic noise in a time domain. Due to space environment changes, temperature variations, and various disturbances, the periodic noise is time-varying, and the spectral peaks vary over time, which will affect the prediction accuracy of the SAM. In this paper, we investigate the periodic noise and its variations present in BDS clock offsets, and improve the clock prediction model by considering the periodic variations. The periodic noise and its variations over time are analyzed and quantified by short time Fourier transform (STFT). The results show that both the amplitude and frequency of the main periodic term in BDS clock offsets vary with time. To minimize the impact of periodic variations on clock prediction, a time frequency analysis model (TFAM) based on STFT is constructed, in which the periodic term can be quantified and compensated accurately. The experiment results show that both the fitting and prediction accuracy of TFAM are better than SAM. Compared with SAM, the average improvement of the prediction accuracy using TFAM of the 6 h, 12 h, 18 h and 24 h is in the range of 6.4% to 10% for the GNSS Research Center of Wuhan University (WHU) clock offsets, and 11.1% to 14.4% for the Geo Forschungs Zentrum (GFZ) clock offsets. For the satellites C06, C14, and C32 with marked periodic variations, the prediction accuracy is improved by 26.7%, 16.2%, and 16.3% for WHU clock offsets, and 29.8%, 16.0%, 21.0%, and 9.0% of C06, C14, C28, and C32 for GFZ clock offsets.

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“…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%

An Improved QZSS Satellite Clock Offsets Prediction Based on the Extreme Learning Machine Method

Zhou

Zhu

et al. 2020

IEEE Access

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The Japanese Quasi-Zenith Satellite System (QZSS) has been developed as a GPS (Global Positioning System) complementary system to improve positioning accuracy in the Asia-Pacific region. However, the accuracies of ultra-rapid predicted clocks are not high enough for real-time applications, so it is still a challenge problem. This paper focuses on the clock predictions of the new QZSS constellation. Based on the QZSS clock periodic characteristics, an improved clock prediction method combining the spectrum analysis model (SAM) and extreme learning machine (ELM) is proposed with abbreviation as iELM. The key parameters of iELM are selected carefully including the number of hidden layer nodes and activation function. Further, the input length of the iELM network is optimized and discussed thoroughly. For the purpose of assessing the performance of the proposed algorithm, the clock prediction accuracies are compared among GBU-P (the ultra-rapid predicted orbits/clocks provided by GFZ (Deutsches GeoForschungsZentrum)), SAM and iELM methods. It is demonstrated that iELM keeps in a high level of accuracy below 1.0ns with the predicted time increasing from 0 to 18h, and a little larger during the next six hours. And the QZO (Quasi-Zenith Orbit) satellites perform better than GEO (Geostationary Earth Orbit) satellite. Furthermore, precise point positioning (PPP) for both static and kinematic modes are experimentally studied for 13 IGS (International GNSS (Global Navigation Satellite System) Service) MGEX (Multi-GNSS Experiment) stations in the longitude range between 100°E and 180°E. In the static PPP mode, the iELM method is verified to be effective for the GPS/QZSS constellation as positioning accuracy is improved by 28.3%, 57.7% and 47.4% on average in the east, north and up component with respect to GPS/QZSS GBU-P results. Nearly 70.0% stations achieve sub-decimeter accuracy in the vertical component. As for the kinematic PPP, the iELM method based on GPS/QZSS observations performs much better than the others for shorter convergence time and better positioning accuracy. Compared with GPS/QZSS GBU-P, iELM takes at most a half time to get convergence, and the accuracy is enhanced by 27.6%, 23.7% and 13.9% on average in the east, north and up direction respectively.

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

Cited by 12 publications

References 40 publications

Improving Clock Prediction Algorithm for BDS-2/3 Satellites Based on LS-SVM Method

Improving Clock Prediction Algorithm for BDS-2/3 Satellites Based on LS-SVM Method

BDS Satellite Clock Prediction Considering Periodic Variations

An Improved QZSS Satellite Clock Offsets Prediction Based on the Extreme Learning Machine Method

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