Determination and analysis of front-end and correlator-spacing-induced biases for code and carrier phase observations
Bibliographic record
Abstract
Abstract Global navigation satellite system (GNSS) biases preclude integer ambiguity resolution and degrade positioning accuracy if they are not corrected in GNSS precise applications. Biases in GNSS positioning applications occur because of imperfections and physical limitations in satellite and receiver hardware. Consequently, these biases will affect the accuracy of positioning solutions, particularly for precise applications due to the existence of biases in the code and carrier phase observations. Various types of biases between systems, frequencies, and satellites have been defined and analyzed. In addition, receiver biases are often assumed to be eliminated by differencing observations between satellites, although this is not always true. This paper investigates the determination of the receiver front-end and correlator-spacing-induced biases in code and carrier phase observations with a focus on how receiver front-end and correlator spacing affect the code and carrier phase measurements, and how such biases vary with respect to the use of different correlator spacings and frontends. Firstly, oscillator, front-end chip, and ADC-induced biases, as well as their observability, will be discussed. Several groups of datasets with different frontends have been collected and used to determine the inter-front-end (including oscillator, chip, and ADC) pseudorange and carrier phase biases. Then, a software receiver that allows the tracking of a satellite with a series of different correlator spacings has been developed to assess measurement biases with different datasets. The results show that the inter-front-end biases and correlator-spacing-induced biases are significantly different among satellites, which can not be ignored during the GNSS positioning. This is because the single-difference of measurements between satellites can not eliminate all these biases. The results with the software receiver connected to different frontends and different correlator spacings indicate that the satellite-dependent biases depend on the configuration of the in-receiver hardware and software.
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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 | 0.000 | 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".