Performance analysis of UWB localization in multi-floor industrial scenarios
Bibliographic record
Abstract
In industrial environments, precise operator localization is a critical challenge to enhance safety, particularly in confined spaces or hazardous zones requiring rapid interventions. While the Global Positioning System is widely utilized, it is unsuitable for indoor environments. An alternative solution lies in the use of radio frequency devices, with Ultra-Wideband (UWB) technology emerging as one of the most promising methods for precise indoor localization. However, industrial environments raise additional challenges, such as the presence of obstacles that lead to non-line-of-sight (NLOS) conditions and complex spatial configurations. This paper investigates the application of UWB-based localization in multi-floor environments, focusing on the impact of anchor placement on localization accuracy. Through experimental studies, it is demonstrated that UWB enables precise localization, even in complex configurations involving multiple floors or independent zones. Furthermore, this study highlights the importance of exploring anchor placement optimization while accounting for NLOS effects, paving the way for significant improvements in localization performance within complex industrial environments.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".