A feature scaling based k-nearest neighbor algorithm for indoor positioning system
Why this work is in the frame
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Bibliographic record
Abstract
With the increasing popularity of wireless local area network infrastructure, Wi-Fi fingerprint based indoor positioning systems have received considerable attention in recent years. In the literature, most existing work in this area focuses on techniques that match the vector of radio signal strength (RSS) values reported by a mobile device to the fingerprints collected at predetermined reference points (RPs) by comparing the similarity (measured based on RSS difference) between them. However, these existing techniques fail to consider the fact that equal RSS differences at different RSS levels may not mean equal distances in reality. To address this issue, in this paper, we propose a feature scaling based k-nearest neighbor algorithm (FS-kNN) for improved localization accuracy. In FS-kNN, we build a novel RSS-based feature scaling model, which introduces signal-level-scaled weights in the calculation of effective signal distance between signal vector reported by mobile device and existing fingerprints. Experimental results show that FS-kNN can achieve an average error distance as low as 1.93 meters, which is superior to previous work.
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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