Wi‐Fi Fingerprint‐Based Indoor Mobile User Localization Using Deep Learning
Why this work is in the frame
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Bibliographic record
Abstract
In recent years, deep learning has been used for Wi‐Fi fingerprint‐based localization to achieve a remarkable performance, which is expected to satisfy the increasing requirements of indoor location‐based service (LBS). In this paper, we propose a Wi‐Fi fingerprint‐based indoor mobile user localization method that integrates a stacked improved sparse autoencoder (SISAE) and a recurrent neural network (RNN). We improve the sparse autoencoder by adding an activity penalty term in its loss function to control the neuron outputs in the hidden layer. The encoders of three improved sparse autoencoders are stacked to obtain high‐level feature representations of received signal strength (RSS) vectors, and an SISAE is constructed for localization by adding a logistic regression layer as the output layer to the stacked encoders. Meanwhile, using the previous location coordinates computed by the trained SISAE as extra inputs, an RNN is employed to compute more accurate current location coordinates for mobile users. The experimental results demonstrate that the mean error of the proposed SISAE‐RNN for mobile user localization can be reduced to 1.60 m.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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