A UHF Passive RFID Tag Position Estimation Approach Exploiting Mobile Robots: Phase-Only 3D Multilateration Particle Filters With No Unwrapping
Bibliographic record
Abstract
Radio-frequency identification is one of the Internet of Things’ most promising technologies and has been recently used in combination with mobile robots for logistics in business and retail applications. This manuscript deals with the localization of passive UHF RFID tags within industrial environments employing receiving antennas mounted on a mobile robot by using multilateration techniques that exploit narrowband phase-delay measurements. Two distinct Particle Filter approaches are presented to solve the 3D multilateration problem online and take advantage of a synthetic aperture created by the motion of the robot in the environment. One of the methods can operate in the presence of acquisition jumps since it does not rely on an unwrapping technique. Experimental results show promising performance concerning the recent literature. Moreover, the presented approach enables robust estimations concerning signal loss due to communication disturbances in noisy environments, typical of the industrial setting.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".