THE UNIVERSITY OF CALGARY On-The-Fly GPS Ambiguity Resolution with Inertial Aiding
Bibliographic record
Abstract
Ambiguity resolution is the key to precise positioning applications with GPS (Global Positioning System) carrier phase measurements. The objective of this research is to investigate the feasibility of integrating high quality inertial data into GPS On-the-Fly (OTF) ambiguity resolution and cycle slip detection. A key factor to an ambiguity search procedure is the accuracy of the float ambiguities. The superior navigation accuracy over the short term provided by a high quality INS (Inertial Navigation System) can improve the precision of the initial float ambiguities, and the integration of inertial data into the ambiguity filtering process can yield more accurate float ambiguities and thus facilitate the integer search procedure. In this thesis, inertial aiding in the FASF (Fast Ambiguity Search Filtering) ambiguity resolution is examined in theory under three INS/GPS integration scenarios: loose and tight coupling integration in a decentralized filter structure, and an augmented master filter integration in a centralized filter structure. The ambiguity dilution precision (ADOP), which measures the accuracy of the float ambiguities and the size of the search space, is investigated. To evaluate the performance
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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 | 0.001 | 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 | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.047 | 0.007 |
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".