Performance Analysis of MEMS-based RISS/PPP Integrated Positioning for Land Vehicles
Bibliographic record
Abstract
Automated vehicles (AVs) have gained increasing interest over the past few years. A crucial feature of these vehicles is an accurate and robust positioning system. Global navigation satellite system (GNSS) precise point positioning (PPP) can achieve decimeter-level accuracy without the need for local reference stations. Nevertheless, the solution availability is affected by GNSS signal outages, which frequently occur in AVs driving scenarios. The integration with an inertial navigation system (INS) provides a continuous positioning solution; however, high-end inertial sensors are bulky and expensive. The recent improvements to the low-cost micro-electro-mechanical (MEMS) sensors opened the way to utilize these sensors in high-precision applications. The objective of this work is to investigate the performance of integrating dual-frequency PPP with low-cost MEMS sensors for land vehicles on highways and suburban areas. Furthermore, the Reduced Inertial Sensor System (RISS) is used instead of the traditional INS system. RISS uses two horizontal accelerometers and one vertical gyroscope in addition to the vehicle odometer, eliminating two gyroscopes and one accelerometer compared to the full IMU system. The lower number of sensors contributes to reducing the error growth over time and reducing the system cost and complexity. A road test was performed that included suburban areas and highway driving with multiple overpasses. The result showed that the developed PPP/RISS system was able to achieve decimeter-level rms positioning errors and a maximum of one meter horizontal positioning error.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".