Cosine similarity based fingerprinting algorithm in WLAN indoor positioning against device diversity
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Bibliographic record
Abstract
The fingerprinting location method is commonly used in WLAN indoor positioning system. Device diversity (DD) which leads to Received Signal Strength (RSS) value difference between the users' device and the reference device is becoming an increasingly important factor impacting the positioning accuracy. Thus, the device diversity is a key problem gained more and more attention in fingerprinting location system recently, which introduces many uncertainties to the positioning result. Traditionally, the Euclidean distance is widely adopted in fingerprinting method. However, when encountering with RSS value difference caused by device diversity, the localization performance is degraded significantly. Due to this problem, our paper proposes a method employing cosine similarity instead of the Euclidean distance to improve the positioning accuracy about 13.15% higher within 2 meters when device diversity exists in the positioning. The experiment results show that the proposed method presents a good performance without the expenses of computation caused by calibration method which is employed in many previous works.
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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