A New Velocity Meter Based on Hall Effect Sensors for UAV Indoor Navigation
Bibliographic record
Abstract
This paper presents a new approach to enhance the indoor navigation of unmanned aerial vehicles (UAVs). During indoor flight missions, UAVs rely on the integration of inertial navigation systems (INS) and aiding sensors measurements to estimate the UAV's navigation states. These aiding measurements may be position, velocity, or attitudes. For indoor environments, the presence of absolute positioning systems, such as Wi-Fi, as an aiding sensor is not always guaranteed. Therefore, alternative aiding sources are required to mitigate the drift exhibited in INS and enhance the navigation accuracy. Different sensors have been utilized to aid INS. These aiding sensors should be carefully chosen, as they should not exceed the cost, weight, size, and power consumption limitations of the UAV. This paper presents a new utilization of Hall effect sensors to act as a flying air odometer (Air-Odo) for quadcopter UAV and to aid the INS with velocity update. Testing results confirmed the capabilities of the new approach to aid the navigation solution through real experiments, with velocity update applied to an extended Kalman filter. The new approach enhanced the navigation solution by 98% when compared with the stand-alone INS solution.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".