A Study on measurement of characteristics of Transmitted Receiver Module’s combined antenna for a RADAR system calibration
Bibliographic record
Abstract
The antenna system of the active phased array radar is composed of parallel transmission and reception paths. The transmission and reception paths of the parallel structure include nonlinear errors. And a long time test measurement is performed using a large measuring facility to align them electrically. If components in parallel path are replaced, a radar system has to calibrate a difference of characteristic to maintain the alignment as confirmed by the test before. In the case of an array radar system with many transmitted receiver module(TRM)s, an efficient calibration algorithm is required, which takes into account operating time and initial alignment error. Especially in the case of TRM, which is designed as an integrated antenna considering the maintenance property, The transmission characteristics of the TRM can be measured, and the measured data can be utilized for the calibration of the antenna system. In this antenna system calibration method, the measuring equipment should be managed, because the influence of the characteristics of the measuring equipment on the compensation state should be minimized. In this paper, we explain a measurement method for the characteristics of a TRM designed as an integrated antenna, and propose a method to compensate the characteristics of measuring equipment. And the effectiveness of the proposed calibration method is verified through simulation.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".