Integration of GNSS and INS with a phased array antenna
Bibliographic record
Abstract
High attenuation, blockage and severe multipath fading in urban environments, under dense canopy or jamming attacks degrade accuracy, continuity, availability and integrity of GNSS services. GNSS/INS integration and antenna array beamforming approaches both provide certain levels of protection against these challenging circumstances in different ways and are studied in the literature; however, their combination of them has received less attention. This research studies different strategies to combine a GNSS antenna array with an inertial navigation system. The focus is on the integration of ultra-tight and tightly coupled GNSS/INS with a distortionless GNSS beamformer. It is shown that a tighter integration of a phase array antenna with INS and GNSS not only has all the benefits of array processing and INS in dealing with challenging environments, but also can provide external information for attitude parameters, and therefore, the overall performance of the integrated system is improved. To verify the applicability of the integrated system and to evaluate its performance, two sets of data have been collected and analyzed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".