Learning RSSI Feature via Ranking Model for Wi-Fi Fingerprinting Localization
Bibliographic record
Abstract
Wi-Fi fingerprinting is widely used in indoor localization due to the ubiquitous availability of Wi-Fi infrastructure in indoor environments. The basic assumption of fingerprinting localization is that the received signal strength indicator (RSSI) distance is consistent with the location distance. However, due to the fluctuation of Wi-Fi signals in indoor environments, the nearest neighbors selected using the RSSI distance may not be those whose corresponding locations are nearest to the target, which could lead to a large localization error. In this paper, we propose a novel fingerprinting method for indoor localization by transforming raw RSSI into features with a learned non-linear mapping function. To learn such mapping function, we design a triple loss function that measures the difference between the rank of RSSI distance and that of location distance. By minimizing the loss function iteratively, we can learn the non-linear mapping function with the gradient boosting regression forest (GBRF) method. Experiments have been conducted in a complex environment and experimental results show that our method outperforms the state-of-the-art methods.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".