Real-time GPS satellite orbit and clock estimation based on OpenMP

Abstract

Real-time precise GNSS satellite orbit and clock products are the prerequisite of real-time GNSS-based applications. To obtain real-time GNSS satellite orbit and clock, three approaches exist currently, namely, the prediction-estimation approach, the prediction-correction approach and the estimation approach. Different from the former two approaches, which are based on the predicted orbit, the last approach estimates orbit and clock in an integrated way, thus it is the most rigorous one. However, the simultaneously estimation of both orbit and clock parameters makes it very time-consuming. In this contribution, the extended Kalman filter with parallel computation proposed for real-time GPS satellite clock estimation (Gao et al., 2017) is introduced to improve the computational efficiency. In the introduced method, the epoch observations are processed sequentially and the covariance update process is accelerated with the Open Multi-Processing. With observation data from about 70 globally distributed stations spanning days 001–003 of 2018, the real-time GPS orbit and clock are estimated for validation. The epoch average processing time of the introduced method achieves around 2.9 s on average with 16 CPU cores, while that of the traditional method without Open Multi-Processing is about 4.1 s. When compare the estimated orbit and clock to the IGS final products, the daily constellation-mean RMS of orbit achieve 2.7, 5.7, 4.9 cm for the radial, along-track and cross-track respectively, while the daily constellation-mean STD of the clock is about 0.10 ns. The numerical experiments indicate that the introduced method is able to provide real-time sub-decimeter GPS orbit and clock within 10.0 s considering the time for data collection and corrections broadcast.