Received signal strength calibration for handset localization in WLAN
Bibliographic record
Abstract
Terminal localization for indoor Wireless Local Area Networks (WLANs) is critical for the deployment of location based services inside of buildings. A key challenge in localization arises when more than one mobile device is used for collecting received signal strength (RSS) measurements due to the existence of inconsistencies in the signal measurement hardware from different devices. This paper focuses on developing a calibration method to bring RSS measurements from different source devices to comparable levels by the means of an affine transformation. This calibration method is automated based on the Expectation-Maximization (EM) algorithm to find the effective transformation parameters in real time. The results show that the estimated locations are within 2m of the true locations for laptop and handset localization when an affine transformation is used and that measurements from only 11 locations were needed for the automated calibration algorithm to obtain the transformation parameters. The results indicate that this calibration technique yields high accuracy levels in localization and that automated RSS measurement calibration is feasible.
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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 | 0.000 | 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 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".