Improving prediction performance of GPS satellite clock bias based on wavelet neural network |
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| Authors: | Yupu Wang Zhiping Lu Yunying Qu Linyang Li Ning Wang |
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| Institution: | 1.Institute of Surveying and Mapping,Information Engineering University,Zhengzhou,China;2.State Key Laboratory of Geo-information Engineering,Xi’an,China;3.Institute of Arts and Sciences,Information Engineering University,Zhengzhou,China |
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| Abstract: | As one of the IGS ultra-rapid predicted (IGU-P) products, the orbit precision has been remarkably improved since late 2007. However, because satellite atomic clocks in space show complicated time–frequency characteristics and are easily influenced by many external factors such as temperature and environment, the IGU-P clock products have not shown sufficient high-quality prediction performance. An improved prediction model is proposed in order to enhance the prediction performance of satellite clock bias (SCB) by employing a wavelet neural network (WNN) model based on the data characteristic of SCB. Specifically, two SCB values of adjacent epoch subtract each other to get the corresponding single difference sequence of SCB, and then, the sequence is preprocessed through using the preprocessing method designed for the single difference sequence. The subsequent step is to model the WNN based on the preprocessed sequence. After the WNN model is determined, the next single difference values at the back of the modeling sequence are predicted. Lastly, the predicted single difference values are restored to the corresponding predicted SCB values. The simulation results have shown that the proposed prediction principle based on the single difference sequence of SCB can make the WNN model simple in architecture and the predicting precision higher than that of the general SCB prediction modeling. The designed preprocessing method specific to the single difference of SCB is able to further improve the prediction performance of the WNN model by reducing the effect from outliers. The proposed SCB prediction model outperforms the IGU-P solutions at least on a daily basis. Specifically, the average prediction precisions for 6, 12 and 24 h based on the proposed model have improved by about 13.53, 31.56 and 49.46 % compared with the IGU-P clock products, and the corresponding average prediction stabilities for 12 and 24 h have increased by about 13.89 and 27.22 %, while the average prediction stability of 6 h is nearly equal. |
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