EVALUATION OF DYNAMIC AD-HOC UWB INDOOR POSITIONING SYSTEM
Bibliographic record
Abstract
Abstract. Ultra-wideband (UWB) technology has witnessed tremendous development and advancement in the past few years. Currently available UWB transceivers can provide high-precision time-of-flight measurements which corresponds to range measurements with theoretical accuracy of few centimetres. Position estimation using range measurement is determined by measuring the ranges from a rover or a dynamic node, to a set of anchor points with known positions. However, building a flexible and accurate indoor positioning system requires more than just accurate range measurements. The performance of indoor positioning system is affected by the number and the configuration of the anchor points used, along with the accuracy of the anchor positions.This paper introduces LocSpeck, a dynamic ad-hoc positioning system based on the DW1000 UWB transceiver from Decawave. LocSpeck is composed of a set of identical nodes communicating on a common RF channel, forming a fully or partially connected network where the positioning algorithm run on each node. Each LocSpeck node could act as an anchor or a rover, and the role could change dynamically during the same session. The number of nodes in the network could change dynamically, since the firmware of LocSpeck supports adding and removing nodes on-the-fly. The paper compares the performance of the LocSpeck system with commercially available off-the-shelf UWB positioning system. Different operating scenarios are considered when evaluating the performance of the system, including cases where collaboration between the two systems is considered.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".