Architecture and System Performance of SPAN -NovAtel's GPS/INS Solution
Bibliographic record
Abstract
As a GPS receiver manufacturer, NovAtel is in a unique position to build a GPS/INS navigation system. The Synchronized Position Attitude Navigation (SPAN) system is based on OEM4 receiver technology combined with an Inertial Measurement Unit (IMU). The IMU integration is tightly coupled with access to the GPS receiver core. The integrated system provides real time position, velocity and attitude. GPS outages can be seamlessly bridged, enabling more reliable navigation through challenging environments like urban canyons. Additionally, GPS performance is improved with the integration of inertial measurements, allowing for faster signal reacquisition and faster return to a fixed integer carrier phase solution after signal outage. The real time solution is computed on board the receiver and raw data can be simultaneously logged for post-processing. Post processing is performed by NovAtel’s Waypoint Inertial Explorer package. This paper discusses NovAtel's approach to INS/GPS system architecture. To demonstrate the performance of the SPAN system, data will be collected under real world conditions in a land vehicle. Test results will show system performance with various levels of GPS aiding and with wheel sensor aiding. The real time solution will be compared to the post-processed solution. Methods to deal with the constraints of real time will be discussed. The accuracy benefits of a post-processed solution will be demonstrated as well.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".