Adaptive Path Loss Model for BLE Indoor Positioning System
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Bibliographic record
Abstract
Indoor positioning systems (IPSs) allow the location and tracking of mobile devices in indoor environments where the global positioning system (GPS) does not provide satisfactory results. In model-based IPSs, it is common to use signal propagation models to estimate distances between anchor nodes and mobile devices using the received signal strength indicator (RSSI). However, using fixed parameters in the path loss model to characterize the signal in large-scale scenarios results in the degradation of the positioning accuracy. In this article, we propose the adaptive model (ADAM) positioning system, a model-based IPS that chooses the best anchor nodes to benefit the positioning computation and uses different parameters for the log-distance model to represent the signal in different regions and conditions of the scenario. Then, we estimate a single, more precise position using a data fusion technique. Our proposal does not require training nor prior knowledge of the best parameters for each region. We evaluated the performance of our proposed system in a real-world, large-scale environment using Bluetooth-based mobile devices. Our results clearly show that ADAM can locate mobile devices with an average error of 2.93 m in relation to the real position, which is 23% better than literature-based models using fixed parameters for the entire environment.
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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