Integration of Multi-Constellation GNSS Precise Point Positioning and MEMS-Based Inertial Systems Using Tightly Coupled Mechanization
Bibliographic record
Abstract
We develop a new integrated navigation system, which integrates multi-constellations GNSS precise point positioning (PPP), including GPS, GLONASS and Galileo, with low-cost micro-electro-mechanical sensor (MEMS) inertial system, for precise positioning applications. To integrate GNSS and the MEMS-based inertial system, the process and measurement models are developed. Tightly coupled mechanism is adopted, which is carried out in the GNSS raw measurements domain. Both un-differenced and between-satellite single-difference (BSSD) ionosphere-free linear combinations of pseudorange and carrier phase GNSS measurements are processed. Rigorous models are employed to correct GNSS errors and biases. The GNSS inter-system biases are considered as additional unknowns in the integrated error state vector. The developed stochastic model for inertial sensors errors and biases are defined based on first order Gaussian Markov process. Extended Kalman filter is developed to integrate GNSS and inertial measurements and estimate inertial measurements biases and errors. Two field experiments are executed, which represent different real-world scenarios in land-based navigation. The data are processed by using our developed Ryerson PPP GNSS/MEMS software. The results indicate that the proposed integrated system achieves decimeter to centimeter level positioning accuracy when the measurement updates from GNSS are available. During complete GNSS outages the developed integrated system continues to achieve decimeter level accuracy for up to 30 seconds while it achieves meter-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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".