Integration of INS and Un-Differenced GPS Measurements for Precise Position and Attitude Determination
Bibliographic record
Abstract
The integration of GPS and INS observations has been extensively investigated in recent years. Current systems are commonly based on the integration of INS data and the double differenced GPS measurements from two GPS receivers in which one is used as a reference receiver set up at a precisely surveyed control point and another is as the rover receiver whose position is to be determined. The requirement of a base receiver is to eliminate the significant GPS measurement errors related to GPS satellites, signal transmission and GPS receivers by double differencing measurements from the two receivers. With the advent of precise satellite orbit and clock products, the un-differenced GPS measurements from a single GPS receiver can be applied to output accurate position solutions at centimetre level using a positioning technology known as precise point positioning (PPP). This then opens an opportunity for the integration of un-differenced GPS measurements with INS for precise position and attitude determination. In this paper, a tightly coupled un-differenced GPS/INS system will be developed and described. The mathematical models for both INS and un-differenced GPS measurements will be introduced. The methods for mitigating GPS measurement errors will also be presented. A field test has been conducted and the results indicate that the integration of un-differenced GPS and INS observations can provide position and velocity solutions comparable with current double difference GPS/INS integration systems.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".