Task selection and scheduling in multifunction multichannel radars
Bibliographic record
Abstract
In a multifunction radar, several tasks with differing parameters, such as tracking and surveillance, must be scheduled on a timeline. In an overload situation, the scheduling can become a challenging problem, as some of the tasks may need to be delayed or even dropped. With recent advancements in multichannel radars, e.g., multifrequency radars, it is possible to perform multiple tasks on different channels in parallel. This leads to us considering the NP-hard problem of optimal task scheduling for multiple channels. We extend previously proposed heuristic approaches to the multichannel case; but our main contribution is an optimal solution based on the branch-and-bound (B&B) method. The heuristics are suboptimal but have the advantage of low computational complexity. On the other hand, the optimal solution provides significantly better performance than the heuristics, but has high computational burden and is likely impractical for real-time scheduling. However, the B&B approach does provide the performance upper bound on heuristics and can be used to train a cognitive task 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".