Two-dimensional transmit beamforming for MIMO radar with sparse symmetric arrays
Bibliographic record
Abstract
Multiple-input multiple-output (MIMO) radar using one-dimensional transmit arrays has been thoroughly investigated in the literature. In this paper, we consider the MIMO radar problem in the context of two-dimensional (2D) transmit arrays. In particular, we address the problem of transmit beamforming design using 2D arrays with symmetrically missing elements. This situation is encountered in practice when some of the array elements are assigned for a different purpose, e.g., for communication purposes. We cast the transmit beamforming problem as an optimization problem that minimizes the difference between a desired transmit beampattern and the actual one while satisfying constraints such as uniform transmit power across the array elements, sidelobe level control, etc. Moreover, different transmit beams can be enforced to have rotational invariance with respect to each other, a property that enables efficient computationally cheap 2D direction finding at the receiver. Semi-definite relaxation is used to recast the optimization problem as a convex one that can be solved efficiently using the interior point optimization methods. Simulations are used to validate the proposed method.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".