IEEE 802.11 WLAN Based Real-Time Location Tracking in Indoor and Outdoor Environments
Bibliographic record
Abstract
In this paper we propose an IEEE 802.11 wireless LAN (WLAN) based location tracking system for indoor and outdoor environments. The system is implemented using the received signal strength indication (RSSI) measurements and training-data based estimation techniques. Methods for acquiring, filtering and interpreting wireless data are discussed with emphasis on how they will be applied to wireless tracking. Training-data based estimation is performed by taking a series of RSSI measurements at known training points and then approximating the current location using the nearest neighbor algorithm. Propagation-based distance estimation is based on the Log-distance path loss model. Experiments are conducted for both training and propagation models to estimate the distance of the user from a known access point in indoor and outdoor locations. The experimental results that show the indoor and outdoor location estimation using the propagation model and training data are presented.
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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".