Assessing Probability of Correct Ambiguity Resolution in the Presence of Time-Correlated Errors
Bibliographic record
Abstract
ABSTRACT: To meet their accuracy requirements, many applications require the resolution of the carrier phase ambiguities to their integer values. However, the process of ambiguity resolution is ultimately based on statistical values and therefore has an associated probability of being performed correctly. This paper develops a method of computing the probability of correctly fixing the ambiguities in the presence of time correlated errors. A new Kalman filter for use in the presence of time correlated observations is reviewed and adapted for carrier phase GPS applications. In terms of correlated errors, consideration is specifically given to ionosphere, zenith troposphere, and multipath effects; all modeled as first-order Gauss-Markov processes. Simulated results, based on the covariance matrix of the float ambiguities, are used to provide theoretical bounds on the probability of correct fix using several levels of observation error variances and time correlations. Results indicate that increased correlation of observations reduces the ability to resolve ambiguities suggesting that if observation correlation is ignored, overly optimistic probabilities of correct ambiguity resolution will be obtained.
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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 | 0.010 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".