Low-Cost, Triple-Frequency, Multi-GNSS PPP and MEMS IMU Integration for Continuous Navigation in Simulated Urban Environments
Bibliographic record
Abstract
Abstract In this research, a next-generation, low-cost triple-frequency GNSS, microelectromechanical (MEMS) based inertial measurement unit (IMU), and a patch antenna was used to obtain decimeter-level accuracy in a suburban and urban environment. A unique combination of the low-cost hardware and software constraining was used to bridge the GNSS gaps in an urban environment to provide a continuous, accurate, and reliable position solution that is novel and has not been previously published. The low-cost navigation system demonstrates less than a decimeter-level accuracy in the presence of a sufficient number of satellites. During half a minute of introduced GNSS signal loss, the overall rms of the algorithm is 10–40% better than dual-frequency PPP with IMU, as the satellite availability reduces. The results obtained during partial GNSS availability indicate a significant step forward in the low-cost navigation area for applications like low-cost autonomous vehicles, intelligent transportation systems, etc. that demand a decimeter level of accuracy.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".