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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".