Comparing detection performance of polarization and spatial diversity for indoor GNSS applications
Bibliographic record
Abstract
GPS signal detection is limited in indoor areas due to signal attenuation and multipath fading. Considering diversity systems can provide two major benefits: increasing overall average received signal power and decreasing signal fading margins by combining independent signal sources. In this paper, detection performance of spatial and polarization diversity systems applied to the received GPS signals in indoor environments is investigated. Herein, the polarization diversity is formed using two different combinations of orthogonal polarized antennas: one with Right Hand Circular Polarized (RHCP) and Left Hand Circular Polarized (LHCP) antennas and another one by vertical and horizontal antennas. Theoretical comparison of these antenna diversity structures is investigated along with real GPS signal collection in different indoor locations to evaluate their performance experimentally. The diversity gain metric has been introduced to quantify the performance of the combining method theoretically and experimentally. Since diversity gain is a function of correlation coefficient and average signal input, these parameters are measured and compared as well for the target diversity systems.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".