Fingerprinting Localization Using Ultra-Wideband and Neural Networks
Bibliographic record
Abstract
In this paper, the interest in localization is basically for security issues in a big industry that is still considered to be one of the most dangerous working places, the mines. Mines conditions make solutions based on TOA (time of arrival), AOA (angle of arrival), or even RSS (received signal strength) subject to big errors. The system we discuss relies on fingerprinting technique to overcome those inconveniences. The use of UWB with its high temporal resolution and its multipath beneficial properties will constitute the basic pillar in overcoming much of indoor localization problems. Neural networks with their interpolation characteristics have the role of replacing any database correlator (or search engine) that usually exists in fingerprinting techniques. Measurements of the channel response in the investigated medium, in addition to the choice of the appropriate parameters, will be explored. Evaluation of the neural network performance and comparison with other indoor techniques will help identifying the utility of the proposed system. Finally, future work to be done will be described.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".