Robust, Multi-Constellation, Multi-Frequency Precise Point Positioning for Instantaneous cm-level Positioning
Bibliographic record
Abstract
Achieving instantaneous centimetre-level 3D accuracy for Global Navigation Satellite System (GNSS) multi-constellation, multi-frequency Precise Point Positioning (PPP) with ambiguity resolution without local augmentation poses significant challenges due to multipath, atmospheric refraction, and hardware noise. This research reduces York-PPP engine limitations by separating the BeiDou-2 and BeiDou-3 GNSS constellations clock terms in the design matrix and expanding the uncombined Decoupled Clock Model from triple to quadruple frequency measurements for BeiDou-3 constellation. Processing of geodetic measurements show overall horizontal positioning errors reduced by 77% (from 20.8 cm to 4.7 cm) and 88% (from 22.8 cm to 2.8 cm) for float and fixed solutions, respectively. Furthermore, this research investigates outliers in epoch-by-epoch PPP solutions, delving into potential causes such as signal-to-noise ratio (SNR), pseudorange multipath and noise etc. Additionally, principal component analysis with Hotelling’s T-squared method is employed to detect major outlier sources. Results indicate that satellite elevation angle, signal strength, and the code-minus-carrier observable, which includes ionospheric refraction and pseudorange multipath, significantly impact positioning solution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".