Initial Position Estimation Using RFID Tags: A Least-Squares Approach
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Bibliographic record
Abstract
The GPS has revolutionized how people, vehicles, and objects are positioned. The GPS, however, has limitations. It will only work well where a signal can be received and will not work underground, in tunnels, or even some buildings. Obtaining an accurate position estimate in these areas must therefore use alternate methods that do not rely on GPS. Promising research from the field of robotics provides an alternative approach to positioning, using a technique known as simultaneous localization and mapping (SLAM). The challenge for the SLAM algorithm is that the initial position given to the algorithm must be accurate. This paper investigates the concept of using an array of RF identification (RFID) tags placed at known positions to provide the initial position of the stationary vehicle to the SLAM algorithm. A least-squares (LS)-based position estimator is presented and evaluated in an experiment conducted in an underground potash mine and an indoor environment at the University of Saskatchewan. The estimator's average error is calculated using models with a varied number of parameters. It was found that both environments attain the best results with five model parameters that were obtained from data taken in the same environment. The results suggest that RFID-based positioning, using this LS approach, has the potential to provide relatively accurate and low-cost initial position estimation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it