Performance of GPS and Partially Deployed BeiDou for Real-Time Kinematic Positioning in Western Canada
Bibliographic record
Abstract
China has completed the development of the first phase of its BeiDou satellite navigation system, which contains fourteen operational satellites at of the end of 2012. This thesis implements the Real-Time Kinematic (RTK) positioning using the integrated GPS and BeiDou system in comparison to the GPS-only system to evaluate whether current BeiDou, which is designed to provide regional coverage in the Asia-Pacific region, can augment the GPS system in North America. Three types of measurements, L1-only, L1 and L2, and Wide-lane combination, were tested over short (10 m), medium (20 km), and long (40 km) baselines to give a comprehensive performance analysis in terms of RTK positioning. The signal quality and measurement precision of BeiDou are presented and compared with those of GPS. The availability and geometry, float and fixed positioning accuracy, convergence time of float ambiguities, Time To First-Fix (TTFF) the ambiguities, and the actual success rate of the ambiguities for the GPS/BeiDou system and the GPS-only system are compared to analyze the improvements brought by the current BeiDou system. Results reveal that BeiDou measurements have the same level of precision as GPS. Adding BeiDou improves the float positioning accuracy and accelerates the convergence time of the ambiguities over three different baselines. The actual success rates are improved and the TTFFs are reduced by inducing BeiDou in the L1-only and L1 and L2 cases. The use of the wide-lane combination brings great improvements in the ambiguity resolution particularly over longer baselines. With the additional BeiDou measurements, the actual success rates do not improve since they all maintain at 100%, but the TTFFs are reduced greatly over the three baselines.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".