Fusion-based Satellite Clock Bias Prediction Considering Characteristics and Fitted Residue

Lu, Jicang; Zhang, Chao; Zheng, Yong Tao; Wang, Ruopu

doi:10.1017/s0373463317001035

Cited by 12 publications

(7 citation statements)

References 27 publications

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“…They show the incorporation of fixed-period harmonics, especially for the GPS satellite clocks, which provides a very accurate predicting model. Even though the SAM is an enhancement of the QPM with periodic terms included, it is pointed out that the periodic functions have to be determined reasonably based on a very long clock time series [17,18]. Meanwhile, researches indicate that clock parameters can be estimated by employing the KF method with a small amount of recent data.…”

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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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: 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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“…As a result of these factors, usually clock offsets, including clock bias, frequency bias, and drift, contain strong non-linear and stochastic features which make the short-term precise modeling and prediction rather difficult [13,15]. Despite such difficulties, numerous studies were carried out, and several models were established for clock prediction, e.g., quadratic polynomial (QP) model, spectrum analysis (SA) model, Kalman filter (KF) and neural network (NN) model [16,17].…”

Section: Introductionmentioning

confidence: 99%

“…They show the incorporation of fixed-period harmonics, especially for the GPS satellite clocks, provides a very accurate predicting model. Even though the SA model with periodic terms included is an improvement to quadratic polynomial model, it is pointed out that the periodic function has to be determined reasonably by a rather long clock time series [14,17]. Meanwhile, Epstein et al achieve an accuracy of 8 to 9 ns by predicting GPS clocks over six hours with a KF approach, but the estimation is not reliable due to occurring frequency jumps [23].…”

Section: Introductionmentioning

confidence: 99%

Improving Short Term Clock Prediction for BDS-2 Real-Time Precise Point Positioning

Zhou

Wen

et al. 2019

Sensors

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Although there are already several real-time precise positioning service providers, unfortunately, not all users can use the correction information due to either cost of the service and limitation of their equipment or out of the service coverage. An alternative way is to enhance the accuracy of the predicted satellite clocks for precise real-time positioning. Based on the study of existing prediction models, an improved model combing the spectrum analysis (SA) and the generalized regression neural network (GRNN) model is proposed especially for BeiDou satellite navigation system (BDS)-2 satellites. The periodic terms and GRNN-related parameters including length and interval of sample data, as well as a smooth factor, are optimized satellite by satellite to consider satellite-specific characteristics for all the fourteen BDS-2 satellites. The improved model is validated by comparing the predicted clocks of existing models and the improved model with precisely estimated ones. The bias of the predicted clock is within ±0.5 ns over three hours and better than that of the other models and can be used for several real-time precise applications. The clock prediction is further evaluated by applying clock corrections to precise point positioning (PPP) in both static and kinematic mode for eight IGS (International GNSS Service) MGEX (Multi-GNSS Experiment) stations in the Asia-Pacific region. In the static PPP, the improved model is validated to be effective, and position accuracies of some IGS MGEX stations achieve more than 30.0% improvements on average for each component, which enables us to obtain sub-decimeter positioning. In the kinematic PPP, the improved model performs much better than the others in terms of both the convergence time and the position accuracy. The convergence time can be shortened from 1–2 h to 0.5–1 h, while the position accuracy is enhanced by 15.4%, 21.6% and 19.3% on average in east, north and up component, respectively.

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“…Strandjord et al took advantage of the repeatability of the clock variations and the potential of observed variations to predict the clock errors and the results implied that the methods could improve the predicted precision, which is of vital importance for the real-time GPS PNT [10]. Lu et al put forth a fusion predicted method based on 4 typical prediction models and proved that the method improved accuracy and stability [11]. Further, exponential smoothing (ES) method has been widely used in time series prediction, like business, transportation, industry, meteorology, etc.…”

Section: Introductionmentioning

confidence: 99%

A Novel Short-Medium Term Satellite Clock Error Prediction Algorithm Based on Modified Exponential Smoothing Method

Liu

Chen

Zhang

et al. 2018

Mathematical Problems in Engineering

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Clock error prediction is important for satellites while their clocks could not transfer time message with the stations in earth. It puts forth a novel short-medium term clock error prediction algorithm based on modified differential exponential smoothing (ES). Firstly, it introduces the basic double ES (DES) and triple ES (TES). As the weighted parameter in ES is fixed, leading to growing predicted errors, a dynamic weighted parameter based on a sliding window (SW) is put forward. And in order to improve the predicted precision, it brings in grey mode (GM) to learn the predicted errors of DES (TES) and combines the DES (TES) predicted results with the results of GM prediction from error learning. From examples' analysis, it could conclude that the short term predicted precisions of algorithms based on ES with GM error learning are less than 0.4ns, where GM error learning could better the performances slightly. And for the medium term, it could conclude that the fusion algorithm in DES (TES) with error learning in GM based on SW could reduce the predicted errors in 35.37% (66.34%) compared with DES (TES) alone. In medium term clock error prediction, the predicted precision of TES is worse than DES, which is roughly in the same level of GM.

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Fusion-based Satellite Clock Bias Prediction Considering Characteristics and Fitted Residue

Cited by 12 publications

References 27 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

Improving Short Term Clock Prediction for BDS-2 Real-Time Precise Point Positioning

A Novel Short-Medium Term Satellite Clock Error Prediction Algorithm Based on Modified Exponential Smoothing Method

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