Scheduling Optimization with Virtual Task for Radar Resource Management
Bibliographic record
Abstract
This paper proposes a new method for optimizing radar task scheduling, which is built upon the assistance of zeropriority virtual tasks. Specifically designed for under-loaded radar situations, the method initially generates several virtual tasks (VTs) without any assigned priorities. These VTs, along with the original real tasks, are then fed into the Random-Shifted-Start-Time (RSST) radar scheduler for scheduling. The VTs are strategically placed as temporal spacers among real tasks to minimize their unwanted shifts. RSST process is iterated multiple times to obtain an optimized task schedule with reduced cost. Numerical simulations demonstrate that the schedule determined by the proposed method is up to 20 times less costly than that derived from the traditional Earliest-Start-Time (EST) algorithm, or 8 times less than the schedule when using the RSST algorithm alone, under certain loading conditions. With the computational time maintained below 5 milliseconds, the proposed method is both efficient and practical for real-time radar missions.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".