The Chinese BeiDou Navigation Satellite System has shown potential for precise positioning with a comparable accuracy to that of the Global Positioning System (GPS) at the centimeter level for the horizontal component and the subcentimeter level for the vertical component. However, a longer convergence time limits the performance of BeiDou precise point positioning (PPP) compared to the GPS PPP solutions. In this study, we applied the tropospheric delay information, derived from the European Centre of Medium-Range Weather Forecasts (ECMWF) analysis and prediction data, into the simulated real-time BeiDou-only and BeiDou/GPS PPP to augment the solutions. Observations from stations in Southeast Asia, which are capable of tracking the BeiDou constellation from the International GNSS Service (IGS) Multi-GNSS Experiment and Pilot Project (MGEX) network, are processed with different strategies: the standard PPP and the introduced ECMWF-augmented PPP with analysis and prediction data, respectively. The positioning results demonstrate that the ECMWF-augmented BeiDou-only and BeiDou/GPS PPP methods using prediction data perform as well as those using analysis data. In the case of BeiDou-only PPP scenarios, remarkable advancements of 80.6% for the convergence time are achieved by two ECMWF-augmented PPP solutions with respect to the standard PPP method. For the positioning accuracy, the two proposed augmented PPP methods attain 6.6 cm in three-dimensional (3D) accuracy when the standard PPP solution get converged (10 cm), representing a remarkable improvement of 34%. As for the north/east/up component, improvements of 14.7 and 8% for positioning accuracy are obtained for the north and east components, respectively, while a remarkable improvement of 37.3% is achieved for the vertical component. In terms of the BeiDou/GPS PPP solutions, the ECMWF-augmented PPP scenarios attain over 10% improvements in 3D accuracy in all processing session lengths. These improvements totally come from the vertical component, whereas almost no enhancements are obtained in two horizontal components.
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Mobile phone data have become a critical data source for transportation research. While a cell-id trajectory was routinely reorganized by International Mobile Subscriber Identity (IMSI), it potentially allows to analyze transportation behaviors and social interaction of total population, with a full temporal coverage at low cost. However, cell-id trajectory is often sparse due to low reporting frequency and uncertainness of mobile holders’ position. So, the cell-id trajectory refinement has been recognized as challenging work to further facilitate trajectory data mining. This paper presents a comprehensive approach to identify cell-id trajectories of public service vehicles (PSVs) from large volume of trajectories and further refines these cell-id trajectories by a heuristic global optimization approach. The modified longest common subsequence (LCSS) method is used to match a cell-id trajectory and a public transportation route (PTR) and correspondingly calculates their similarities for determining whether the trajectory is PSV mode or not. Taking full advantages of the nature of a PSV tends to move on the PTR in uniform motion to meet a prescript visit to stops, a heuristic global optimization approach is deployed to build a spatiotemporal model of a PSV motion, which estimates new locations of cell-id trajectories on the PTR. The approach was finally tested using Beijing cellular network signaling datasets. The precision of PSV trajectory detection is 90%, and the recall is 88%. Evaluated by our GNSS-logged trajectories, the mean absolute error (MAE) of refined PSV trajectories is 144.5 m and the standard deviation (St. Dev) is 81.8 m. It shows a significant improvement in comparison of traditional interpolation methods.
The cycle-slip detection is the prerequisite for achieving the utmost precise carrier-phase measurements for the low earth orbit (LEO) spaceborne Global Navigation Satellite System (GNSS) receivers. However, the issue has not yet been solved entirely due to the limitations of traditional cycle-slip detection methods. In the paper, we proposed a novel cycle-slip detection method based on the second-order time-difference phase ionosphere-free (IF) or wide-lane (WL) observations with dynamic orbital constraints. Nominal orbits at the dm-level accuracy and the earth gravity field of at least 30 degrees and orders (d/o) within 30 s or higher sampling rate data could guarantee the proposed method reliable for the flying altitude of near 500 km of LEO satellites. To validate the proposed method effectiveness, numeric experiments with comparisons to the traditional cycle-slip detector based on the Melbourne–Wübbena (MW) combination are carried out with well-chosen measurements for the LEO satellite under ionospheric activities of different levels. The results, in terms of the correctness and misjudgment of the cycle slips detected and the precision of estimated orbit, indicate that, on the one hand, the detector based on the proposed phase IF test parameters are superior to one based on the proposed phase WL test parameters or the MW combinations under any level of ionospheric activities; on the other hand, under highly active ionospheric activities, the proposed phase WL cycle-slip detector discovers a fewer number of real cycle slips, which are flagged by the reference method introduced in the paper, but more misjudgments than the MW cycle-slip detector does, which leads to the inferior of the WL cycle-slip detector. However, the phase WL cycle-slip detector performs slightly better than the MW cycle-slip detector under moderately active and very quiet ionospheric activities. In view of the developing multi-frequency multi-GNSS uncombined observation data scanning, the proposed phase IF test parameter accompanied with the MW combinations is recommended to be applied to the cycle-slip detection if the predictable ionospheric refraction lies at the strongly intensive level of the ionospheric activity. Otherwise, the proposed phase WL test parameter would be involved.
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