Enhancing Navigation in Difficult Environments with Low-Cost, Dual-Frequency GNSS PPP and MEMS IMU
Bibliographic record
Abstract
Abstract The Global Navigation Satellite System (GNSS) Precise Point Positioning (PPP) technology benefits from not needing local ground infrastructure such as reference stations and accuracy attained is at the decimetre-level, which approaches real-time kinematic (RTK) performance. However, due to its long position solution initialization period and complete dependence on the receiver measurements, PPP finds limited utility. The emergence of low-cost, micro-electro-mechanical sensor (MEMS) inertial measurement units (IMUs) has prompted research in integrated navigation solutions with the PPP processing technique. This sensor fusion aids to achieve continuous positioning and navigation solution availability when there are insufficient numbers of navigation satellites visible. In the past, research has been conducted to integrate high-end (geodetic) GNSS receivers with PPP processing and MEMS IMUs, or low-cost, single-frequency GNSS receivers with point positioning processing and MEMS IMUs. The objective of this research is to investigate and analyze position solution availability and continuity by integrating low-cost, dual-frequency GNSS receivers using PPP processing with the latest low-cost, MEMS IMUs to offer a complete, low-cost navigation solution that will enable continuously available positioning and navigation solutions, even in obstructed environments. The horizontal accuracy of the developed low-cost, dual-frequency GNSS PPP with MEMS IMU integrated algorithm is approximately 20 cm. During half a minute of simulated GNSS signal outage, the integrated solution offers 40 cm horizontal accuracy. A low-cost, dual-frequency GNSS receiver PPP solution integrated with a MEMS IMU forms a unique combination of a total low-cost solution, that will open a significant new market window for modern-day applications such as autonomous vehicles, drones and augmented reality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".