A Novel Method for Navigation Satellite Clock Bias Prediction Considering Stochastic Variation Behavior

Lei, Yu; Hu, Zhaopeng; Zhao, Di

doi:10.1007/978-3-642-54740-9_32

Cited by 3 publications

(3 citation statements)

References 8 publications

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“…It can be seen from Figure 1 and existing research (Xu et al, 2016; 2013a; 2013b; Lei et al, 2014) that the combination methods generally did not take fully into account the characteristics of the single models under different conditions, as well as various characteristics of observed SCB. Therefore, it has been difficult to obtain a combination prediction model with a superior and more stable performance under a variety of conditions.…”

Section: Related Workmentioning

confidence: 92%

“…Xu et al (2013a; 2013b) combined QP and a functional network. Lei et al (2014) combined QP and a least squares support vector machine. The principle of these two categories are illustrated in Figure 1, the left panel denotes the former category, and the right panel denotes the latter.…”

Section: Related Workmentioning

confidence: 99%

“…To date, a number of Satellite Clock Bias (SCB) analysis and prediction models or algorithms, such as the Quadratic Polynomial (QP) model (Huang et al, 2014; Wang et al, 2016), Spectrum Analysis (SA) (Heo et al, 2010; Senior et al, 2008), Grey system Model (GM) (Yuan et al, 2008; Zheng et al, 2008), Auto Regressive Integrated Moving Average (ARIMA) model (Xi et al, 2014; Stein and Evans, 1990), Kalman Model (Galleani and Tavella, 2010; Davis et al, 2012; Pratt et al, 2013), Least Square Support Vector Machine (LS-SVM) model (Liu et al, 2013), Wavelet Neural Network (WNN) model (Ai et al, 2016; Wang et al, 2017), etc. In addition, combination prediction methods were also proposed by combining typical multiple models (Lei et al, 2014; Wang et al, 2011; Xu et al, 2016). However, existing models or algorithms generally do not take fully into account the influences of factors such as periodic items, sampling length, stochastic items and so on, which result in low precision and usability.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Zheng

et al. 2018

J. Navigation

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As Satellite Clock Bias (SCB) prediction may be affected by various factors such as periodic items, sampling length, and stochastic items, a fusion-based prediction method is proposed by considering characteristics of SCB and fitted residue. On this basis, an instance algorithm is presented by fusing four typical prediction models. First, we use Empirical Mode Decomposition (EMD) to pre-process and decompose the SCB series into multiple components with various characteristics. Then, we analyse the fitting performance of each model for different components and prediction length, namely short-, mid- and long-term prediction, and select models with the best performance. Next, we analyse fitted residue of the reconstructed SCB, and select the model with the best fitting results. Finally, we fuse the multiple selected models for SCB prediction. The method is tested using Global Positioning System (GPS) precise clock products provided by the International Global Navigation Satellite System Service (IGS). Experimental results show that, compared with single prediction models and existing combination models, the proposed fusion-based prediction method improves accuracy and stability. In particular, the proposed method is more stable and has better performance for mid- and long-term prediction.

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