Abstract
Precise point positioning (PPP) with wide-lane ambiguity resolution (PPP-WAR) can quickly achieve decimeter-level positioning accuracy, which is an essential improvement for real-time PPP in terms of reliability and accuracy. However, current implementations of PPP-WAR are based only on the standard Kalman filter (KF) under the assumption of uncorrelated measurements. As Global Navigation Satellite System measurements are often contaminated by colored noises, a KF considering the time-correlation of measurements (CKF) has the potential to improve the performance of PPP-WAR. In this paper, we proposed a real-time cascading PPP-WAR technique based on the CKF model. A real-time kinematic vehicle experiment is conducted to assess the performance of the proposed real-time PPP-WAR method. On the one hand, we analyze and compare the efficiency of CKF-based cascading PPP-WAR with that of KF-based PPP-WAR. The filter output analysis shows that the proposed CKF method is, overall more efficient than the standard KF method in terms of performance indicators, such as wide-lane ambiguity fix-rate, time-to-first-fix, ambiguity residual statistics and correctness of the filter’s stochastic model. On the other hand, we evaluate the convergence time and positioning accuracy of CKF-based PPP-WAR by comparing it with KF-based PPP-WAR. The results show that the fix-rate of the CKF method increased by 26% compared with the KF-based WAR method. Moreover, the CKF-PPP-WAR can achieve 0.5 m (sub-meter) level with 1–4 s faster, 0.2 m level with 2–20 s faster, 0.1 m level with 5–60 s faster compared with float PPP. On average, CKF-PPP-WAR can achieve decimeter-level accuracy by reducing the convergence time 20% than KF-PPP-WAR, and 25% than float PPP. After convergence, the average positioning accuracy of CKF-PPP-WAR is 2–6 cm better with 25% improvement over float PPP and 1–4 cm better with a 23% improvement over KF-PPP-WAR.
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Data availability
The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.
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Acknowledgements
The financial support from the Natural Sciences and Engineering Research Council of Canada (NSERC) and the Chinese Scholarship Council (CSC) are greatly acknowledged.
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YJ and YG designed the processing strategy and the experiments, developed the algorithms, performed the analysis of the experiments and wrote the paper. PZ, YG, GH supervised the project and contributed to the final version of the manuscript.
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Jiang, Y., Gao, Y., Zhou, P. et al. Real-time cascading PPP-WAR based on Kalman filter considering time-correlation. J Geod 95, 69 (2021). https://doi.org/10.1007/s00190-021-01520-w
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DOI: https://doi.org/10.1007/s00190-021-01520-w