Dual-Polarized Synthetic Antenna Array for GNSS Handheld Applications
Bibliographic record
Abstract
Small portable Global Navigation Satellite System (GNSS) receivers have revolutionized personal navigation through providing real-time location information for mobile users. Nonetheless, signal fading due to multipath remains a formidable limitation and compromises the performance of GNSS receivers. Antenna diversity techniques, including spatial and polarization diversity, can be used to mitigate multipath fading; however, the relatively large size of the spatially distributed antenna system required is incompatible with the small physical size constraints of a GNSS handheld receiver. User mobility inevitably results in motion of the handset that can be exploited to achieve diversity gain through forming a spatially distributed synthetic array. Traditionally, such motion has been construed as detrimental as it decorrelates the received signal undermining the coherent integration processing gain generally necessary for acquiring weak faded GNSS signals. In this paper the processing gain enhancement resulting from a dual-polarized synthetic array antenna, compatible with size constraints of a small handset that takes advantage of any user imposed motion, is explored. Theoretical analysis and experimental verifications attest the effectiveness of the proposed dual-polarized synthetic array technique by demonstrating an improvement in the processing gain of the GNSS signal acquisition operation.
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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 | 0.000 | 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) | 0.002 | 0.001 |
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".