Neural network and fingerprinting-based localization in dynamic channels
Bibliographic record
Abstract
In a harsh indoor environment, fingerprinting localization techniques perform better than the traditional ones, based on triangulation, because multipath is used as constructive information. However, this is generally true in static conditions as fingerprinting techniques suffer degradations in location accuracy in dynamic environments where the properties of the channel change in time. This is due to the fact that the technique needs a new database collection when a change of the channel's state occurs. This paper proposes a method allowing an accurate mobile user's location in time-varying channels when it is difficult or impossible to collect measurements. The system has the ability to generate, from a measured reference database, a new database corresponding to a new channel state. This is done by using measurements of few reference points in conjunction with a tree model data mining technique. The technique uses a regression analysis to learn the temporal predictive relationship between the received signal strength values of the mobile and the reference points in order to generate a new database at a different time state. After generating several databases, corresponding to several time states, an artificial neural network is used for location estimation. Results show low degradation, compared to a static channel, of approximately 7% and 11% at 3 meters in 2D and 3D dynamic environments, respectively.
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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".