Enhanced GNSS Signal Tracking in Fading Environments using Frequency Diversity
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Bibliographic record
Abstract
A new methodology to enhance the GNSS parameter estimation accuracy in multipath fading environments using the frequency diversity reception is proposed herein. In such environments, fading occurrences observed in different frequency bands are independent of each other. Characteristics of frequency diversity reception using GPS L1 and L2C signals in dense fading conditions are first discussed and compared with those of spatial diversity reception. Comparative analyses of characterization metrics between frequency diversity and spatial diversity signals support the argument that the former is as effective as the latter. A frequency diversity based combined tracking approach is then proposed, and the performance is evaluated using the data collected in dense foliage and residential environments. Results show that frequency diversity based combined tracking results in improved performance compared to that of single frequency tracking. 3D position error reduction achieved by the proposed method is 52% in foliage conditions and 50% in residential environments. Copyright © 2017 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 | 0.000 | 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 | 0.001 |
| 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