Short-term satellite clock bias forecast based on complementary ensemble empirical mode decomposition and quadratic polynomial

Tan, Xiaorong; Xu, Jiangning; He, Hongyang; Chen, Ding; Liang, Yifeng; Wu, Miao

doi:10.1080/00396265.2022.2025714

Cited by 4 publications

(3 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The value of

{c}_{2}-{c}_{1}

is defined as the width of confidence interval. More detailed explanation can be found in the literature [28].…”

Section: Resultsmentioning

confidence: 99%

A new GM (1,1) model suitable for short‐term prediction of satellite clock bias

Tan

et al. 2022

IET Radar Sonar & Navi

Self Cite

View full text Add to dashboard Cite

Due to the sensitivity of spaceborne atomic clock to many factors, the variation law of satellite clock bias (SCB) can be regarded as a grey system. The GM (1,1) model is a most classical and basic model of grey system, which has been successfully applied in SCB prediction. Moreover, many improved models have been proposed and widely used in various forecasts since GM (1,1) was generated. However, the prediction performance of these models is not obviously improved compared with the classical models in clock bias prediction. In view of this, a new GM (1,1) model has been come up with in this paper by optimising fitting model and initial condition. The new fitting model is obtained by differentiating time response function of winterisation, and the new initial condition is generated through one or more components of the original clock bias sequence. The authors employ GPS rapid and precise SCB provided by the International GNSS Service (IGS) for prediction experiments. The results show that the new GM (1,1) model is effective and feasible, and its prediction accuracy and stability are enormously better than that of the classical GM (1,1) model, especially for ultra-short-term prediction.

show abstract

“…The value of

{c}_{2}-{c}_{1}

is defined as the width of confidence interval. More detailed explanation can be found in the literature [28].…”

Section: Resultsmentioning

confidence: 99%

A new GM (1,1) model suitable for short‐term prediction of satellite clock bias

Tan

et al. 2022

IET Radar Sonar & Navi

Self Cite

View full text Add to dashboard Cite

show abstract

“…In formula (12), the specific meaning of the parameters such as μ, σ, and so on can be seen in literature [29].…”

Section: Resultsmentioning

confidence: 99%

Improved GM (1,1) Model by Optimizing Initial Condition to Predict Satellite Clock Bias

Tan

et al. 2022

Mathematical Problems in Engineering

Self Cite

View full text Add to dashboard Cite

The variation law of satellite clock bias (SCB) can be regarded as a grey system because the spaceborne atomic clock is very sensitive and vulnerable to many factors. GM (1,1) model is the core and foundation of the grey system, which has been highly valued and successfully applied in SCB prediction since its production. However, there are still some problems to be further studied such as the lack of stability of its prediction effect in practical application. In view of this, an improved GM (1,1) model by optimizing the initial condition has been proposed in this paper so as to increase the prediction performance. The new initial condition is obtained by the weighted combination of the latest and oldest components of the original clock bias sequence. And the weight values of these two components are acquired from a method of minimizing the sum of squares of fitting errors. We adopt GPS rapid precision SCB data provided by the International GNSS Service (IGS) for 15 mins, 30 mins, 1 h, 3 h, 6 h, 12 h, and 24 h prediction experiments. The results show that the improved GM (1,1) model is effective and feasible, and its prediction accuracy and stability are significantly better than those of the traditional GM (1,1) model, ARIMA model, and QP model, even for the SCB signal with obvious fluctuation.

show abstract

“…To improve the timeliness and accuracy of the clock bias products, various forecast models, including mathematical models such as quadratic polynomial model (QP) [14][15] , spectral analysis model (SA), 1-order and 1-variable grey model (GM (1,1)) [16][17] , auto-regressive integrated moving average (ARIMA) [18][19][20] , Kalman filter 21 , as well as neural network models [22][23][24] , have been proposed. QP and SA are widely used due to their clear physical meanings and practicality.…”

mentioning

confidence: 99%

Real-time prediction of the approximate non- homogeneous white exponential law BDS clock bias sequences: modelling and strategy

Tan,

Xu,

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

Precise forecasting of satellite clock bias is crucial for ensuring service quality and enhancing the efficiency of real-time precise point positioning (PPP).The grey model with many benefits is an excellent choice for predicting real-time clock bias. However, the absolute prediction error of a small number of satellites is very high in actual forecasting precess. In order to address this issue, a non-homogeneous white exponential law grey model has been constructed specifically for predicting clock bias sequences with non-homogeneous class ratio approximating constants. Considering that any model is difficult to apply to different kinds of satellite clocks and clock bias signals, an adaptive selection strategy for forecast model is proposed to ensure more excellent prediction accuracy. Afterwards a prediction scenario based on the observed products of the BeiDou satellite navigation system (BDS) is created to demonstrate the effectiveness of the approach described in this article. The outcomes of the method are then compared with those of a single grey model and the ultra-rapid predicted products. The outcomes demonstrate that this strategy's forecast accuracy is less than 1 ns/day and that its prediction uncertainty is much decreased, which may improve data selection for real-time applications like real-time kinematics (RTK) and PPP.

show abstract

Short-term satellite clock bias forecast based on complementary ensemble empirical mode decomposition and quadratic polynomial

Cited by 4 publications

References 15 publications

A new GM (1,1) model suitable for short‐term prediction of satellite clock bias

A new GM (1,1) model suitable for short‐term prediction of satellite clock bias

Improved GM (1,1) Model by Optimizing Initial Condition to Predict Satellite Clock Bias

Real-time prediction of the approximate non- homogeneous white exponential law BDS clock bias sequences: modelling and strategy

Contact Info

Product

Resources

About