THE PERFORMANCE ANALYSIS OF BDS POSITIONING IN NORDIC AREAS BASED ON THE SMARTPHONE
Bibliographic record
Abstract
Abstract. Nowadays, the Global Navigation Satellite System (GNSS) is widely used in many applications. As a new system, China's BeiDou navigation system (BDS) is emerging in recent years. The last satellite of the third generation global BeiDou navigation system (BDS-3) has been successfully launched on June 23 in 2020, which means that BDS can offer navigation services worldwide. We evaluate the quality of BDS signals and analyze the performance of BDS positioning based on the smartphone in Espoo, Finland. The static and kinematic experiments were implemented in the parking lot of the Finish Geospatial Research Institute (FGI) and a highway route in Espoo, respectively. Experimental results show that BDS has good satellite visibility and geometric distribution. The signal carrier-to-noise density ratio (C/N0) of BDS-2 reaches 34.22 dB-Hz, which is comparable to the Global Positioning System (GPS). However, the signal carrier-to-noise density of BDS-3 is slightly lower than BDS-2, which is due to the significant number of BDS-3 satellites at low elevation angles. The horizontal precision of BDS positioning in the static and kinematic experiment is comparable to GPS in the east direction and slightly inferior to GPS in the north direction. However, the BDS shows poor precision in the up direction. In addition, the integration of BDS with other GNSS systems can significantly improve the positioning precision. This study intends to provide a reference for further research on the BDS global Positioning, Navigation, and Timing (PNT) services, particularly for LBS and smartphone positioning.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".