System Design and Performance Analysis of Indoor Real-time Localization using UWB Infrastructure
Bibliographic record
Abstract
Indoor localization and tracking poses a uniquely challenging aspect for asset tracking. Unreliability of traditional positioning systems, such as GNSS, in indoor applications calls for specialized systems to attain similar levels of accuracy and precision. To this end, Real Time Location System (RTLS) based on deployable anchors and tags are typically employed for these applications. The precision and accuracy of such RTLS systems is limited by the chosen anchors and tags. In this paper, the performance of DecaWave’s Ultra-wide-band (UWB) MDEK1001 is evaluated and tested under ideal Line-of-Sight (LOS) and non-LOS (NLOS) scenarios. The effect of obstacles and anchor bias on accuracy is also evaluated. Different anchor deployment configurations are tested to determine the impact of anchor quantity and position on tag position accuracy. Additionally, this paper provides an overview of EMSLab-RTLS client-server generated for real-time location applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".