PoM: RFID Positioning for Real-World Application Using the Power of Mobility
Bibliographic record
Abstract
In many scenarios, we need to identify an object and then locate it within high precision (centimeter or millimeter level). RFIDs have played a significant role in this field. While many state-of-the-art systems have shown good performance, they require expensive hardware or extra time. Based on a previous work, GLAC, we present PoM, a 3D localization system within millimeter-level precision using only COTS RFID devices. Inspired by the same idea, PoM also draws power from mobility, and makes two key technical improvements. First, to the best of our knowledge, PoM is the first localization system that simultaneously adopts Synthetic Aperture Radar (SAR) and Inverse Synthetic Aperture Radar (ISAR) method. In particular, we employ antenna motion to construct SAR and tag's mobility to construct ISAR. Second, we take actual application scenarios into consideration and apply an extra mechanism so that PoM can gain better performance in special situations. Our simulation experiments show that, in high-speed scenarios and other challenging real-world applications, PoM achieves better performance than the original GLAC system.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".