A high-precision short-term prediction method with stable performance for satellite clock bias
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ORIGINAL ARTICLE
A high‑precision short‑term prediction method with stable performance for satellite clock bias Xu Wang1,2 · Hongzhou Chai2 · Chang Wang3 Received: 25 February 2020 / Accepted: 1 August 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Real-time users cannot carry out real-time precise point positioning (RT-PPP) because they cannot receive real-time service (RTS) products from the international GPS service (IGS) in the case of interrupted communication. We address this issue by introducing a stable particle swarm optimized wavelet neural network (PSOWNN) to predict the short-term satellite clock bias in real time accurately. The predicted sequences of the new model are compared with those of the conventional linear polynomial, quadratic polynomial, gray system (GM (1,1)), and Kalman filter models. The results show that the accuracy of the proposed model is better than that of these four models. The average prediction accuracy of the 30-min and 60-min forecasting has improved by approximately (79.3, 82.4, 79.1, 97.4) % and (97.4, 82.9, 87.7, 98.9) % and is better than 0.3 ns during 30-min and 1-h forecasting. The RTS products can thus be replaced with the short-term clock products predicted by the PSOWNN model to meet the precision requirements of RT-PPP. Keywords Satellite clock bias (SCB) · Particle swarm optimization · Wavelet neural network · Prediction
Introduction Precise satellite clock bias (SCB) prediction is a key technical problem in real-time precise point positioning (RT-PPP) (Huang et al. 2014). Most users meet their requirements by obtaining clock products from the international GPS service (IGS). Although the precision of the final satellite clock is high, it cannot meet real-time demands. IGS ultra-rapid predicted (IGU-P) products and broadcast ephemeris can meet these real-time requirements; however, their accuracy is lower than the final IGS clock products (Wang et al. 2020). The IGS launched an open-access real-time service (RTS) in 2013 to provide users with more accurate real-time products. These products use the observation data of IGS stations * Xu Wang [email protected] 1
Liaoning Vocational College of Ecological Engineering, Forestry Institute, 186 Fengyang Road, Sujiatun District, Shenyang 110101, China
2
PLA Strategic Support Force Information Engineering University, No. 62, Kexue Road, Zhengzhou 450001, China
3
School of Civil Engineering, University of Science and Technology Liaoning, No. 185, Qianshan Middle Road, High‑tech Zone, Anshan 114051, China
around the world to calculate the orbital and clock bias and are broadcast to users with a time delay of about 25 s and an accuracy of 0.1–0.15 ns (ns) (http://www.igs.org/rts/monit or). However, the RTS products also have certain limitations. As the RTS products are obtained using a large amount of ground observation, they require much computing resources. At the same time, when communication to the user is poor and the RTS data cannot be obtained, the application of RTPP
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