Synthesis of Linear and Planar Arrays With Minimum Element Selection
Bibliographic record
Abstract
A new method for the synthesis of linear and planar arrays having prescribed beamwidth and sidelobe levels and a minimum number of elements is proposed. In the method, the number of elements in an array is minimized while constraining the amplitude-response error in the mainlobe region, the attenuation in the sidelobe region, and the array dimensions. An iterative constrained optimization method is used where the amplitude-response error is linearly approximated at each iteration while concurrently minimizing a re-weighted L1 norm of the array coefficients. To ensure robustness of the array, we constrain a sensitivity parameter, namely, the white noise gain, to be above a prescribed level. Furthermore, the method also provides the additional flexibility of controlling the array dimensions, symmetry properties, and element positions of the array. Two variants have been developed: In the first variant, both the array coefficients and the positions of the elements are optimized; in the second variant, only the array coefficients are optimized while the elements are fixed at predefined positions. Experimental comparisons with several state-of-the-art competing methods show that the proposed method provides greater flexibility of controlling the robustness, beampattern response error, array dimensions, and element positions while at the same time the number of elements is less than or equal to that of the competing methods.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".