Comparing and Evaluating Indoor Positioning Techniques
Bibliographic record
Abstract
Accurate indoor positioning has been the subject of investigation for many years. Modern smartphones have a wide suite of internal sensors that allow us to measure different signals. However, traditional positioning methods, such as GPS, typically fail when measurements are taken indoors. Many different solutions have been proposed that rely on various data inputs, including Wi-Fi and camera input. Some proposed methods have used auxiliary data inputs such as BLE Beacons. However, any auxiliary input would require additional infrastructure to purchase and maintain, increasing expenses. This paper explores a variety of indoor positioning techniques that do not require any additional infrastructure beyond what is typically found in a commercial environment. This research explores, implements, and measures, through standardized tests, Wi-Fi RSSI, RTT, and marker-based trilateration, as well as, fingerprinting with two separate machine learning models, and also tests an implementation of PoseNet. It compares and contrasts the various methods, categorizing them according to a proposed set of criteria for evaluating a commercially deployable indoor positioning solution. The paper closes with a brief summary of the techniques that were studied and proposes investigation into various related topics and improvements, as well as future directions.
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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".