Design and optimization of indoor optical wireless positioning systems
Bibliographic record
Abstract
Optical wireless (OW) technologies are an emerging field utilizing optical sources to replace existing radio wavelength technologies. The vast majority of work in OW focuses on communication; however, one smaller emerging field is indoor OW positioning. This emerging field essentially aims to replace GPS indoors. One of the primary competing methods in indoor OW positioning is angle-of-arrival (AOA). AOA positioning uses the received vectors from several optical beacons to triangulate its position. The reliability of this triangulation is fundamentally based on two aspects: the geometry of the optical receiver’s location compared to the optical beacon locations, and the ability for the optical receiver to resolve the incident vectors correctly. The optical receiver is quantified based on the standard deviation of the azimuthal and polar angles that define the measured vector. The quality of the optical beacon geometry is quantified using dilution of precision (DOP). This proceeding discusses the AOA standard deviation of an ultra-wide field-of-view (FOV) lens along with the DOP characteristics for several optical beacon geometries. The optical beacon geometries used were simple triangle, square, and hexagon optical beacon geometries. To assist the implementation of large optical beacon geometries it is proposed to use both frequency and wavelength division multiplexing. It is found that with an ultra-wide FOV lens, coupled with the appropriately sized optical beacon geometry, allow for high accuracy positioning over a large area. The results of this work will enable reliable OW positioning deployments.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".