Wireless Access Point Localization Using Nonlinear Least Squares and Multi-Level Quality Control
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Locations of WiFi access points (APs) are important for WiFi positioning when a propagation model is used. The pre-surveyed propagation parameters, such as the path-loss exponent, are usually not available when localizing the APs in a new environment. This letter introduces a novel method that estimates the AP locations and the parameters of the received signal strength (RSS) propagation model simultaneously using the weighted nonlinear least squares (NLLS) method. This method can run on consumer portable devices autonomously in real time without any a-priori information, and eliminate the need of pre-survey. Another contribution of this letter is to introduce a multi-level quality control mechanism, and utilize the statistical testing method in AP localization and propagation parameters (PPs) determination for the first time. Indoor experiments show that the proposed method provided more promising results than previous methods.
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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 | 0.001 |
| Open science | 0.001 | 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