Research on Optimization of UAV Smoke Screen Jamming Bomb Deployment Strategy
Why this work is in the frame
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Bibliographic record
Abstract
In modern air defense operations, unmanned aerial vehicles (UAVs) deploying smoke screen jamming bombs to implement soft kills on incoming missiles is a low-cost and highly mobile means to improve the protection efficiency of fixed targets. Addressing the two core issues of single smoke bomb shielding effectiveness evaluation and single UAV parameter optimization, this study constructs a unified multi-entity kinematic model and a "line-of-sight-sphere intersection" effective shielding criterion, combined with the bisection method to achieve accurate calculation of shielding duration and parameter optimization. By integrating the model framework, under the established parameter scenario (UAV speed 120m/s, deployment delay 1.5s, detonation delay 3.6s), the effective shielding duration is 1.3872s; under the multi-parameter optimization scenario (course angle 180∘, speed 125m/s, deployment delay 2.1s, detonation delay 4.2s), the shielding duration is increased to 4.7612s. Empirical analysis shows that the model has extremely low sensitivity to deployment-detonation delay (relative change rate <0.0025%) and the UAV speed tolerance meets engineering requirements (relative change rate ≤1.25%), providing quantitative support for the formulation of air defense soft kill strategies.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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 it