Novel Device-Free Indoor Human Localization using Wireless Radio-Frequency Fingerprinting
Bibliographic record
Abstract
We propose a novel device-free localization technique for human-object tracking indoors using wireless radiofrequency fingerprint. The received signal-strength indicators (RSSIs) are measured by the receiving antennae as the features for machine learning subject to the Random Forest model. In our proposed approach, both transmitters and receivers are fixed within the room, thus making human(s) free from carrying any transceiver. The placement of receivers will impact on the localization accuracy, and therefore we investigate the effect of receiver placement in this work. We will introduce an optimal receiver placement strategy such that the average communication-link distance can be minimized. Our proposed method is verified through simulations supported by a popular channel-propagation software, Feko. The pertinent experimental results demonstrate that the localization accuracy of 77.50% can be attained by our proposed Random Forest learning system for a 20 m×10 m indoor area divided into eight equi-sized zones where sixteen receiving antennae are placed at their optimal locations along the perimeter of the room.
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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 | 0.001 |
| 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".