A Sensor Fusion-Based Framework for Floor Localization
Bibliographic record
Abstract
Floor localization is at the heart of indoor positioning systems (IPSs) in multi-storey buildings with a variety of commercial, industrial, and health and safety applications. The prevalence of wireless technologies along with the integration of micro-electro-mechanical sensors (e.g., barometers) in handheld devices and wearable gadgets of current vintage has prompted a surge in research and development efforts in the IPS area. Received signal strength (RSS) and barometric altimetry (BA) are two well-known methods of floor localization; however, RSS-based methods lack the required accuracy and BA-based methods are prone to random errors due to local changes in the air pressure, e.g., from approaching weather systems. Fusion of BA and RSS is a viable solution for floor localization; nevertheless, available fusion algorithms are rather heuristic. In this paper, a theoretical framework is developed for fusing BA and Wi-Fi RSS measurements. The proposed framework involves a novel Monte Carlo Bayesian inference algorithm, for processing RSS measurements, and then fusion with BA using a Kalman Filter scheme. As demonstrated by our experimental results, the proposed sensor fusion algorithm achieves >99% floor localization accuracy. The algorithm does not require new infrastructure and has low computational complexity, hence, can be readily integrated into various state-of-the-art mobile devices.
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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".