High-Precision Beam Selection and Scheduling for Multitarget Tracking in Netted Phased Array Radar Systems
Bibliographic record
Abstract
Advances in phased array radar (PAR) technology have enabled modern radar systems to control beams with extreme agility. In multitarget tracking (MTT) scenarios, efficient allocation of beam resources is essential to maintain optimal tracking performance. High-precision radar systems often employ narrow beams to achieve superior angular accuracy. However, the use of narrow beams increases the chance of target miss-detections, thereby violating conventional tracking assumptions and challenging existing beam scheduling methods. To address the narrow-beam effect, a filter-based beam steering approach is proposed, which leverages the information of missed detections to facilitate rapid target localization. The expected posterior entropy reduction (EPER) associated with the narrow-beam steering is derived and an approximation method is proposed to enable its application in beam scheduling. Furthermore, an optimization framework and a corresponding solution technique are proposed for joint beamwidth selection and narrow-beam scheduling for MTT. Simulation results demonstrate the superior performance of the proposed scheduler.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".