Coordinated radar resource management for networked phased array radars
Bibliographic record
Abstract
A phased array radar has the ability to rapidly and adaptively position beams and adjust dwell times, thus enabling a single radar to perform multiple functions, such as surveillance, tracking and fire control. A radar resource manager prioritises and schedules tasks from the various functions to best use available resources. Networked phased array radars that are connected by a communication channel are studied. This study considers whether coordinated radar resource management (RRM), which exploits the sharing of tracking and detection data between radars, enhances performance compared with independent RRM. Two types of distributed management techniques for coordinated RRM are proposed, with each type characterised by varying amounts of coordination between the radars. A two‐radar network and 30‐target scenario are modelled in the simulation tool Adapt_MFR, to analyse the performance of the two coordinated RRM techniques against the baseline case of independent RRM. Results indicate that the coordinated RRM techniques achieve the same track completeness as independent RRM, while decreasing track occupancy and frame time. Therefore, coordinated RRM can improve reaction time against threats, at the expense of sending data across a communication channel. The performance of coordinated RRM for a communication channel with errors is also modelled and analysed.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".