Exploring Signals on L5/E5a/B2a for Dual-Frequency GNSS Precise Point Positioning
Bibliographic record
Abstract
Due to its nature, Precise Point Positioning (PPP) depends on the GNSS measurements and quality of satellite correction products used to relatively quickly provide precise and accurate positions. With the rapid evolution of Global Navigation Satellite Systems (GNSSs), new frequencies and signals are being broadcast, which have a positive impact on PPP performance. This paper presents, for the first time, a comprehensive analysis of PPP performance from these new GPS, Galileo and BeiDou-2/3 signals, which are not yet commonly used for PPP, with correct mitigation of errors such as the estimation of GPS Block-IIF L5 variations. Satellite orbits and clocks, as well as GPS Block-IIF L5 corrections, are estimated in real-time using DLR's RETICLE engine, while the user processing is performed with York University's PPP engine. First, as a reference, PPP performance is assessed on widely used signals: GPS L1/L2, Galileo E1/E5a, and BeiDou-2/3 B1-2/B3. Horizontal and vertical rms of 2.3 and 2.6 cm, respectively, are achieved in static processing and 5.4 and 7.5 cm in kinematic processing after 1 h of processing using real-time satellite correction products. The compatibility of BeiDou-2 and BeiDou-3 on the shared B1-2/B3 frequencies is analyzed and discrepancies in the receiver clock are found. Next, since all three constellations share two common frequencies, the paper focuses on analyzing PPP performance of GPS, Galileo and BeiDou-3 on [L1, E1, B1] at 1575.42 MHz and [L5, E5a, B2a] at 1176.45 MHz. Horizontal and vertical rms of 6.9 and 7.1 cm are achieved in kinematic processing. The effect of the known GPS Block-IIF L5 biases is studied as well, as it is shown to affect the receiver position and clock, as well as the ionospheric estimates and ambiguities. Average improvements of 15% and 20% in the horizontal and vertical rms, respectively, are observed when these biases are mitigated.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".