Precise GNSS Positioning with Time-differenced Carrier Phases at Variable Sampling Rates
Bibliographic record
Abstract
Abstract. Most Global Navigation Satellite System (GNSS) receivers typically have a sampling rate at 1Hz. However, variable sampling rates are required for optimal performance in different dynamic applications. For example, high sampling rates are crucial for precise tracking of high dynamic platforms such as unmanned aerial vehicle (UAV) navigation. Higher sampling rates help decrease positioning interval time and improve travel distance measurements especially when moving on a curvy route. On the contrary, lower sampling rates help saving power consumption and computation. Currently there is barely literature presenting the impact of sampling rates on positioning accuracy and what sampling rate is required for different vehicle dynamics considering the positioning accuracy and computational load. In this study, we extend TDCP to estimate position at different sampling rates and with high accuracy. We investigate and develop a GNSS software-defined radio (SDR) receiver to implement variable sampling-rate capability at low-cost. This approach provides flexibility, customization and scalability since the GNSS SDR can be reconfigured or updated via software to support multiple GNSS signals, systems or new techniques without requiring costly hardware modifications. The variable-rate phase observations from the GNSS SDR will be applied to form time-differenced carrier phase (TDCP) observations for precise positioning. The test results show that the GNSS-TDCP algorithm can achieve absolute precise positioning, but the positioning error will drift over time. Besides, the research results on the impact of different sampling rates on the positioning performance can help us to select an appropriate sampling rate for GNSS-TDCP system.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".