Self-Contained Antenna Array Calibration using GNSS Signals
Bibliographic record
Abstract
Antenna array processing techniques require calibration algorithms that often rely on the availability of signal sources at known locations, a good knowledge of the array manifold, or a reference antenna. An alternative is provided by GNSS signals that provide the location of their sources as part of their navigation data. In this paper, a projection methodology using GNSS signals is proposed for the calibration of antenna arrays. The Gram Schmidt process is used along with the properties of the signal steering vectors to determine linear relationships between the recovered signals and the calibration parameters. The obtained system of equations is then solved in the Minimum Mean Square Error (MMSE) sense leading to the estimated calibration parameters. The proposed algorithm accounts for signal gain/phase mismatches and mutual coupling between array elements. Finally, the effectiveness of the proposed technique and its suitability for beamforming and Direction-of-Arrival (DoA) applications is supported by several experiments performed using live GPS signals and a GNSS software receiver. Copyright © 2012 Institute of Navigation.
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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 | 0.003 |
| 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 | 0.001 |
| 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".