Joint Antenna Selection and Beamforming for Area Surveillance With Spatially Distributed Array Radar
Bibliographic record
Abstract
This article addresses the joint optimization problem of antenna selection and beamforming design for a spatially distributed array radar (SDAR) used for area surveillance, while meeting spatial response and surveillance requirements. We first derive the mathematical relationships between detection probability and key SDAR parameters, including antenna selection and beamforming weights. The surveillance area, defined as a portion of a hemisphere delimited in azimuth and polar angles, is split into a grid of smaller cells that can each be covered by a single beam. For each angular cell, we then seek to minimize the number of antennas being employed for irradiation, while achieving a desired spatial response and target detection probability. As the formulated optimization problem is a nonconvex mixed-integer nonlinear programming problem, we propose a joint antenna selection and beamforming design algorithm based on the alternating direction method of multipliers (ADMM) to solve it effectively. Specifically, the optimization problem is transformed into an augmented Lagrangian problem based on the ADMM framework by introducing a series of auxiliary variables. We proceed by decomposing the resulting problem into two intertwined subproblems for which an iterative solution is developed, hence enabling an efficient solution of the overall problem wherein both beamforming weights and antenna selection are optimized jointly. Simulation results show that the proposed algorithm can deliver excellent performance in terms of minimizing the antenna resource while reliably meeting the given spatial response and surveillance requirements.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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 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".