Performance of Antenna Array Calibration in Multipath Environments
Bibliographic record
Abstract
Antenna array processing is gaining attention in the GNSS community due to its ability to mitigate different types of interference. Array calibration is the first step towards implementation of most of the distortionless beamforming techniques. In the case of a GNSS, antenna array calibration can be performed using live signals. Since several satellite signals are available from different directions at the same time, the calibration process can incorporate all available GNSS signals. It is required to have a data set without any electronic interference or multipath to avoid errors in the calibration process. However, multipath is a major challenge for calibration and exists almost everywhere. The carrier phase measurements used to estimate the calibration parameters are affected by multipath, which introduces significant gain and phase errors. Due to this, the calibration parameters estimated would normally be erroneous. Carrier phase multipath in GNSS is periodic in nature with zero mean. This feature is exploited herein to improve calibration in multipath environments. By filtering the relative phase measurements between the antenna elements over several multipath periods, estimation of calibration parameters can be improved. As multipath period is longer for a static user than for a moving one, averaging time required to remove multipath is longer in the static case. This is demonstrated through simulated and live GNSS signals.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".