A Portable Vehicular Navigation System Using High Sensitivity GPS Augmented with Inertial Sensors and Map-matching
Bibliographic record
Abstract
<div class="htmlview paragraph">A Global Positioning System (GPS) technology development known as high sensitivity GPS (HSGPS) can significantly improve availability in challenging environments such as in urban canyons where standard GPS performance is extremely poor. However, this technology could produce higher measurement noise, multipath and cross-correlation errors resulting in position errors of hundreds of metres in such cases. The use of internal filtering with “heavy” constraints provides better results in some cases but may result in major biases and overshooting effects in other cases. This paper develops a portable vehicle navigation system by aiding a standalone HSGPS receiver with self-contained inertial sensors and map-matching. Since traditional GPS error estimation methods are shown to be invalid in urban canyon environments for HSGPS, nontraditional data fusion algorithms are needed for augmenting HSGPS with self-contained sensors (<span class="xref">MacGougan et al, 2002</span>). A unique map-matching technique is presented that uses both position and velocity measurements and works with a vehicle dynamics model. The model provides a means to assess the quality of HSGPS measurements and therefore an integration mechanism for inertial sensors. The integrated system was tested in a suburban area as well as in a downtown core with buildings of 10 to 50 stories and mask angles of up to 80 degrees. The system performance under severe GPS signal degradation and multipath conditions is presented.</div>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".