ENHANCED UAV NAVIGATION USING HALL-MAGNETIC AND AIR-MASS FLOW SENSORS IN INDOOR ENVIRONMENT
Bibliographic record
Abstract
Abstract. The use of Unmanned Aerial Vehicles (UAVs) in many commercial and emergency applications has the potential to dramatically alter several industries, and, in the process, change our attitudes regarding their impact on our daily lives activities. The navigation system of these UAVs mainly depends on the integration between the Global Navigation Satellite Systems (GNSS) and Inertial Navigation System (INS) to estimate the positions, velocities, and attitudes (PVT) of the UAVs. However, GNSS signals are not always available everywhere and therefore during GNSS signal outages, the navigation system performance will deteriorate rapidly especially when using low-cost INS. Additional aiding sensors are required, during GNSS signal outages, to bound the INS errors and enhance the navigation system performance. This paper proposes the utilization of two sensors (Hall-magnetic and Air-Mass flow sensors) to act as flying odometer by estimating the UAV forward velocity. The estimated velocity is then integrated with INS through Extended Kalman Filter (EKF) to enhance the navigation solution estimation. A real experiment was carried out with the 3DR quadcopter while the proposed system is attached on the top of the quadcopter. The results showed great enhancement in the navigation system performance with more than 98% improvement when compared to the free running INS solution (dead-reckoning).
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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) | 0.000 | 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 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".