A Composite Model for Indoor GNSS Signals: Characterization, Experimental Validation and Simulation
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Bibliographic record
Abstract
In this paper, the problem of characterizing and simulating indoor Global Navigation Satellite Systems (GNSS) signals is addressed. A detailed methodology for characterizing the temporal/spatial behavior of indoor GNSS signal amplitude is first presented. The proposed methodology is then used to extract the signal amplitude and its empirical Probability Density Function is compared against several standard distributions. From the analysis, it emerges that the slow and fast fading components affecting received GNSS signals need to be modeled separately. In this respect, a composite Rice/Log-Normal model able to effectively capture the behavior of the indoor signal amplitude is considered. Several experiments have been conducted and the validity of the composite model has been validated against measurements.It is shown that the spectra of the slow and fast fading components can be effectively modeled using a fourth order low-pass Butterworth filter. A simulation scheme is finally suggested for the generation of indoor GNSS signals. Copyright © 2012 Institute of Navigation.
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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 | 0.001 | 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 | 0.002 |
| 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