Antenna Placement in Compressive Sensing Radar using Binary Optimization
Bibliographic record
Abstract
Compressive sensing has allowed for the improve-ment of angular resolution in radar technology, which involves two aspects: sparse signal recovery and measurement matrix design. Assuming a sparse target scene, compressive sensing radar depends solely on the design of the measurement matrix to possess certain properties, such as satisfying the restricted isometry property (RIP) and low coherence. The design of the measurement matrix depends on the location of the antennas. In this work, we consider the antenna placement problem in compressive sensing radar. The problem is interpreted as a binary program, where we propose to solve it directly using a heuristic binary optimization algorithm. The proposed binary differen-tial evolution (BDE) algorithm is able to navigate the search space with relatively high diversity while still refining promising candidates. Results illustrate the superiority of approaching the problem directly using BDE rather than resorting to relaxation approaches in the literature.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".