Integration of multi-constellation GNSS precise point positioning and MEMS-based inertial system for precise applications
Bibliographic record
Abstract
This dissertation develops a low-cost integrated navigation system, which integrates multi-constellation global navigation satellite system (GNSS) precise point positioning (PPP) with a low-cost micro-electro-mechanical sensor (MEMS)-based inertial system for precise applications. Both undifferenced and between-satellite single-difference (BSSD) ionosphere-free linear combinations of pseudorange and carrier phase measurements from three GNSS constellations, namely GPS, GLONASS and Galileo, are processed. An improved version of the PF, the unscented particle filter (UPF), which combines the UKF and the PF, is developed to merge the corrected GNSS satellite difference observations and inertial measurements and estimate inertial measurements biases and errors. The performance of the proposed integrated system is analyzed using real test scenarios. A tightly coupled GPS PPP/MEMS-based inertial system is first developed using EKF, which shows that decimeter-level positioning accuracy is achievable with both undifferenced and BSSD modes. However, in general, better positioning precision is obtained when BSSD linear combination is used. During GPS outages, the integrated system shows submeter-level accuracy in most cases when a 60-second outage is introduced. However, the positioning accuracy is improved to a few decimeter- and decimeter-level accuracy when 30- and 10-second GPS outages are introduced, respectively. The use of UPF, on the other hand, reduces the number of samples significantly, in comparison with the traditional PF. Additionally, in comparison with EKF, the use of UPF improves the positioning accuracy during the 60-second GPS outages by 14%, 13% and 15% in latitude, longitude and altitude, respectively. The addition of GLONASS and Galileo observations to the developed integrated system shows that decimeter- to centimeter-level positioning accuracy is achievable when the GNSS measurement updates are available. In comparison with the GPS-based integrated system, the multi-constellation GNSS PPP/MEMS-based inertial system improves the latitude, longitude and altitude components precision by 24%, 41% and 41%, respectively. In addition, the use of BSSD mode improves the precision of the latitude, longitude and altitude components by 23%, 15% and 13%, respectively, in comparison with the undifferenced mode. During complete GNSS outages, the developed integrated system continues to achieve decimeter-level accuracy for up to 30 seconds, while it achieves submeter-level accuracy when a 60-second outage is introduced.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".