The prediction of Satellites' Clock Bias (SCB) plays an important role in optimizing the clock bias parameters in navigation messages, meeting the requirements of real-time dynamic precise point positioning and providing the prior information required for satellite autonomous navigation. Satelliteborne atomic clocks are often affected by many factors in space, which makes it difficult to describe the clocks' bias and behavior with fixed model to achieve reliable high-precision prediction. The composition and characteristics of clock bias for satellite-borne atomic clock are described and analyzed, a clock bias prediction algorithm based on Nonlinear autoregressive model with exogenous input (NARX) recurrent neural network is proposed, the advantages of this model in SCB and other time series prediction are introduced in detail. The SCB data from four different clock types are selected for calculation and analysis. The comparative results show that, for both 6h and 24h forecasts, the accuracy and stability of NARX model are significantly better than three commonly used models, especially in the prediction of satellite cesium atomic clock.
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.
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.
The time scale of the Global Navigation Satellite System (GNSS) is the core element for its position, navigation and timing services. A highly stable atomic clock is essential to ensure the reliability of the GNSS time scale. This study proposed a novel hybrid denoising model combining Variational Mode Decomposition (VMD), K-L divergence, Permutation Entropy (PE), and Savitzky-Golay (SG) filter for satellite atomic clocks. Firstly, the key parameter of VMD is solved efficiently by taking the minimum sum of K-L divergence of decomposed modes as the constraint condition, and the optimised parameters are applied to the decomposition process. On this basis, the PE algorithm is used to determine the modes decomposed by VMD into signal-dominant and noise-dominant components by searching for the mutation of PE value at two adjacent points. Finally, the noise-dominant components are denoised by the SG filter and then reconstructed with the signal-dominant components to form the denoised signal. The analysis of the simulated signal shows that the method can effectively remove noise from the simulated signal, and the resulting denoised signal is similar to the pure signal. Compared with commonly used Ensemble Empirical Mode Decomposition (EEMD) and wavelet denoising methods, the Signal-Noise Ratio (SNR) of the proposed method is improved by 21.2% and 28.9%, and the Root Mean Square(RMS) error is improved by 24.1% and 29.8%, respectively. The results of experimental data testify that the K-L VMD-PE-SG-based denoising method can significantly reduce the dominant noise within one day, thus effectively improving the short to medium-term frequency stability. Compared with the original signal, the stability of the smoothing time within 76800s is generally improved, and the degree of improvement depends on the type of atomic clock and the smoothing time.
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