Sectorial direction finding antenna array with a MLP beamformer
Bibliographic record
Abstract
Mobile terminal (MT) antennas used for satellite links require a tracking system to minimize the degradation caused by the vehicle's motion on the link performance with the satellite. In practice, azimuthal variations are more severe than elevation fluctuations and a one-axis only tracking system, working over a full 360-degree sector, is found to be appropriate. Typically, direction-finding (DF) can be performed with a monopulse system and beam-steering can be achieved mechanically or electronically. Due to the limited electrical size of MT antennas, the sum and difference beams of the standard monopulse system have poor directivity, which makes the tracking system prone to back-lobe locking. Our objective in this paper is to overcome this difficulty by proposing an enhanced monopulse system which is immune to back-lobe locking. This objective was achieved by the implementation of an artificial neural network (ANN) at the output of the antenna array. In this work, a fixed number of three array elements was used. One of the advantages of using an ANN is that the DF system can be trained to compensate for non-ideal behaviour or time degradations of RF circuit components, antenna elements, radomes etc. Such capabilities are demonstrated by the use of the fitting and regression properties of the multilayer neural feedforward with hyperbolic tangent decision functions.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".