Autonomous WLAN heading and position for smartphones
Bibliographic record
Abstract
In recent years, indoor positioning systems have become important and WiFi positioning based on fingerprinting has been gaining a lot of attention in this field. However, surveying for the WiFi fingerprints in a specific area is a labor and time consuming process. In this work, an innovative method is proposed to automatically generate geo-referenced radio maps for Wireless Local Area Networks (WLAN). The Trusted Portable Navigator (T-PN) was used to provide an integrated navigation solution using inertial sensors and Global Navigation Satellite System (GNSS), when GNSS is available. The T-PN provided positions were used to automatically build a radio map when the solution was reliable. Building a radio map by using this method alleviates the cost of expensive surveys and does not require additional time or manual labor. After the radio map is built, it is used for typical fingerprinting-based WiFi positioning. The experimental results show that reasonable positioning accuracy can be obtained with this automatic fingerprint collection method in indoor environments. Nevertheless, the positions calculated in this manner are not accurate enough to calculate a useful heading of the user. In this paper, we propose another innovative method that estimates user heading based on WLAN signals. This estimation technique is based on the mathematical relationship between the rate of change of RSS for the different access points (APs) and the user velocities, and consequently the user heading.
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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".