The failure to detect anomalies and maneuvering of the orbits of navigation satellite sensors will deteriorate the performance of positioning and orbit determination. Motivated by the influence of the frequent maneuvering of BDS GEO and IGSO satellites, this paper analyzes the limitations of existing methods, where BDS orbit maneuvering and anomalies can be detected, and develops a method to solve this problem based on the RMS model of orbit mutual differences proposed in this paper. The performance of this method was assessed by comparison with the health flag of broadcast ephemeris, precise orbit products of GFZ, the O-C values of a GNSS station and a conventional method. The results show that the performance of the method developed in this paper is better than that of the conventional method when the periodicity and trend items are obvious. Meanwhile, three additional verification results show that the method developed in this paper can find error information in the merged broadcast ephemeris provided by iGMAS. Furthermore, from the testing results, it can be seen that the detection of anomaly and maneuvering items do not affect each other based on the robust thresholds constructed in this paper. In addition, the precise orbit of the maneuvering satellites can be determined under the circumstances that the maneuver information detected in this paper is used, and the root mean square (RMS) of orbit overlap comparison for GEO-03/IGSO-03 in Radial, Along, Cross, 1D-RMS are 0.7614/0.4460 m, 1.8901/0.3687 m, 0.3392/0.2069 m, 2.0657/0.6145 m, respectively.

Errors in ultra-rapid UT1-UTC primarily affect the overall rotation of spatial datum expressed by GNSS (Global Navigation Satellite System) satellite ultra-rapid orbit. In terms of existing errors of traditional strategy, e.g., piecewise linear functions, for ultra-rapid UT1-UTC determination, and the requirement to improve the accuracy and consistency of ultra-rapid UT1-UTC, the potential to improve the performance of ultra-rapid UT1-UTC determination based on an LS (Least Square) + AR (Autoregressive) combination model is explored. In this contribution, based on the LS+AR combination model and by making joint post-processing/rapid UT1-UTC observation data, we propose a new strategy for ultra-rapid UT1-UTC determination. The performance of the new strategy is subsequently evaluated using data provided by IGS (International GNSS Services), iGMAS (international GNSS Monitoring and Assessment System), and IERS (International Earth Rotation and Reference Systems Service). Compared to the traditional strategy, the numerical results over more than 1 month show that the new strategy improved ultra-rapid UT1-UTC determination by 29–43%. The new strategy can provide a reference for GNSS data processing to improve the performance of ultra-rapid products.

With the development of near real-time and real-time high-precision global navigation satellite system (GNSS) applications, users’ requirements for the accuracy and stability of ultra-rapid orbits have increased, especially for multi-constellation orbits. Generally, several factors affect the accuracy and stability of multi-GNSS ultra-rapid orbit determination; however, this study is focused only on analyzing the orbit arc length of ultra-rapid orbits of the GPS, GLONASS, BeiDou-2, and Galileo satellites based on the one-step method. To realize nearly ideal conditions in the process of analysis, one full year’s rapid orbit products during 2018 provided by the GeoForschungsZentrum multi-GNSS experiment analysis center were used as fitted observed orbits and also as a reference orbit for comparison to the predicted orbit. In terms of residual analysis of orbit difference and stability analysis of Helmert transformation parameters, the numerical results showed that the optimal orbit arc lengths of quad-constellation medium earth orbit (MEO) satellites for 6 h and 3 h predicted ultra-rapid orbits based on the one-step method were in the range of 44–45 h and 41–44 h, respectively. Overall, for both 6 h and 3 h predictions, when only considering the impact of the orbit arc length on the accuracy and stability of the orbit itself, 44 h can be considered as the overlapping optimal orbit arc length of quad-constellation MEO satellitesʼ ultra-rapid orbits generated by the one-step method. In addition, these ranges of orbit arc length can contribute to the multi-GNSS ultra-rapid orbit determination.

The predicted parts of ultra-rapid orbits are important for (near) real-time Global Navigation Satellite System (GNSS) precise applications; and there is little research on GPS/GLONASS/BDS/Galileo/QZSS five-system ultra-rapid precise orbit determination; based on the one-step method and double-difference observation model. However; the successful development of a software platform for solving five-system ultra-rapid orbits is the basis of determining and analyzing these orbits. Besides this; the different observation models and processing strategies facilitate to validate the reliability of the various ultra-rapid orbits. In this contribution; this paper derives the double-difference observation model of five-system ultra-rapid precise orbit determination; based on a one-step method; and embeds this method and model into Bernese v5.2; thereby forming a new prototype software platform. For validation purposes; 31 days of real tracking data; collected from 130 globally-distributed International GNSS Service (IGS) multi-GNSS Experiment (MGEX) stations; are used to determine a five-system ultra-rapid precise orbit. The performance of the software platform is evaluated by analysis of the orbit discontinuities at day boundaries and by comparing the consistency with the MGEX orbits from the Deutsches GeoForschungsZentrum (GFZ); between the results of this new prototype software platform and the ultra-rapid orbit provided by the International GNSS Monitoring and Assessment System (iGMAS) analysis center (AC) at the Institute of Geodesy and Geophysics (IGG). The test results show that the average standard deviations of orbit discontinuities in the three-dimension direction are 0.022; 0.031; 0.139; 0.064; 0.028; and 0.465 m for GPS; GLONASS; BDS Inclined Geosynchronous Orbit (IGSO); BDS Mid-Earth Orbit (MEO); Galileo; and QZSS satellites; respectively. In addition; the preliminary results of the new prototype software platform show that the consistency of this platform has been significantly improved compared to the software package of the IGGAC.

The polar motion (PM, including two parameters PMx and PMy) ultra-short-term prediction (1–10 days) is demanded in the real-time navigation of satellites and spacecrafts. Improving the PMx and PMy ultra-short-term predictions accuracies are a key to optimize the performance of these related applications. Currently, the least squares (LS)+autoregressive (AR) hybrid method is regarded as one of the most capable approaches for ultra-short-term predictions of PMx and PMy. The Kalman filter has proven to be effective in improving the ultra-short-term prediction performance of the LS+AR hybrid method, but the PMx and PMy ultra-short-term predictions accuracies are still not able to satisfy some related applications. In order to improve the performance of PM ultra-short-term prediction, it is worth exploring the combinations of existing methods. Throughout the existing predicted methods, the LS+multivariate autoregressive (MAR) hybrid method by using the Kalman filter has the potential to improve the accuracy of PM ultra-short-term prediction. In addition, a PM prediction performance analysis of the LS+MAR hybrid method by using the Kalman filter, namely the LS+MAR+Kalman hybrid method, is still missing. In this contribution, we proposed the LS+MAR+Kalman hybrid method for PM ultra-short-term prediction. The data sets for PM predictions, which range from 1 to 10 days, have been tested based on the International Earth Rotation and Reference Systems Service Earth Orientation Parameter (IERS EOP) 14 C04 series to assess the performance of the LS+MAR+Kalman hybrid model. The experimental results illustrated that the LS+MAR+Kalman hybrid method can effectively execute PMy ultra-short-term predictions. The improvement of PMy prediction accuracy can rise up to 12.69% for 10-day predictions, and the improvement of ultra-short-term predictions is 7.64% on average.