Ultrasonic Localization of a Quadrotor using a Portable Beacon
Bibliographic record
Abstract
This paper presents a method for localization of a drone using ultrasonic and radio frequency signals. The system consists of several receiving nodes and a beacon which can be incorporated into a landing pad or onto a moving object in a GPS denied environment. Five receiving nodes are mounted on the arms of a quadrotor, and by measuring the time of arrival from when the ultrasonic signal is produced to when it is received, the distance between each receiver and the beacon can be calculated. The quadrotor requests an ultrasonic signal from the beacon through a radio frequency signal. Through multilateration the position of the quadrotor can be determined relative to the beacon. Threshold detection is used to determine if an ultrasonic signal has arrived and time difference of arrival is used to determine the 3D position using linear least squares to solve the system of equations. A Kalman filter is applied to smooth the position data. A vertical sonar sensor is used to improve height accuracy due to geometric dilution of precision of the receivers. Time division multiple access is applied to avoid interference between the sonar and the localization system. The refresh rate of the system is 5 Hz to allow the signals to decay and avoid multipath propagation. Preliminary experiments show an average accuracy within a one-metre radius of ±9.9 cm with the motors at full throttle. The accuracy of the system improves while hovering over the beacon at a 50 cm radius with a ±6.2 cm accuracy. Autonomous hover has been performed within a GPS denied environment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".