A comparative analysis of indoor localization technologies
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
ABSTRACT Indoor localization holds great potential in various applications such as healthcare facilities, smart buildings, retail and shopping malls, museums, airports, parking lots, etc. Indoor localization systems aim to track and navigate targets in indoor spaces. These systems use various sets of technologies that can be categorized into four groups: Radio Frequency (RF) based, inertial based, optical based, and ultrasound based. To have a fair comparison between different technologies, in this review paper, we divide these technologies into wearable, contactless, and a fusion of different technology groups. All of these methods are proposed and used with different approaches such as machine learning, deep learning, geometric, and signal processing techniques. In this paper, we compare these methods in terms of localization performance, time complexity, coverage, and generalizability. Also, we determine which of these methods are suitable for different applications. It was observed that methods based on contactless RF based technologies outperformed others by showing centimeter level localization accuracy and preserving users' privacy. Additionally, fusing different types of technology can enhance performance compared to when they are used solely. Technologies and techniques that need further research are also discussed in details.
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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 | 0.001 | 0.003 |
| 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