GNSS Precise Point Positioning with Android Smartphones and Comparison with High Performance Receivers
Bibliographic record
Abstract
Precise Point Positioning (PPP) is becoming increasingly used instead of differential GNSS (DGNSS) due to its ease of use. With PPP, precise satellite orbits and clock corrections are calculated using the numerous International GNSS Service (IGS) permanent stations. The IGS network conceptually replaces the reference station(s) used in DGNSS. Models of the ionosphere and the troposphere are used to aid PPP, especially ionospheric models for single frequency users. In addition to 3D position, PPP provides estimates of GNSS time and zenith tropospheric delays. PPP performance is analysed herein as a function of receiver type, observation time and measurement utilized. The high-end receivers used in this study are multi-frequency multi-constellation Leica GS16. The Android phone used in the new Huawei Mate 20X. The measurements that are intercompared are (1) single frequency code, (2) single frequency code and carrier phase, (3) dual frequency code, and (4) dual frequency code and carrier phase. Results in low and high multipath environments are reported. Focus is on the use of GPS and GLONASS constellations because most IGS stations are equipped with such receivers, which is necessary to calculate precise satellite orbits and clock corrections. In order to assess PPP versus DGNSS performance, the results of a test consisting of an array of receivers are reported and analysed.
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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 | 0.002 |
| 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 | 0.000 | 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".