Feasibility Assessment and Enhancement of TOF-Based UWB RTLS for Non-Line-of-Sight Conditions on Construction Sites
Bibliographic record
Abstract
Studies in the past decades have shown that Real-Time Location Systems (RTLS) have strong potential in improving safety and enhancing productivity on construction sites. Ultra-Wide Band (UWB) RTLS in particular, has demonstrated high suitability for construction site conditions due to its higher accuracy, lower dependency on the Line-Of-Sight (LOS), and less signal attenuation and multipath effect. However, higher tolerance to Non-Line-Of-Sight (NLOS) is not equivalent to not having impacts on the accuracy of location estimation. To reduce the impact of NLOS, typically standardized residual analysis of Least Square or Kalman Filter are used (to detect and remove the outliers). However, these methods can only be functional with a minimum of five anchors. The objective of this study is to determine and mitigate the impact of NLOS on location estimation accuracy of TOF-based UWB RTLS when only four anchors are utilized. The proposed mitigation methodology provides an accuracy improvement of more than 60%, which is significant especially for applications such as safety where accuracy is paramount on construction sites.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".