A novel dwelling time design method for low probability of intercept in a complex radar network
Why this work is in the frame
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Bibliographic record
Abstract
To achieve the important tactical requirement of low probability of intercept (LPI) in the complex radar network, dynamically controlling the emission of the radars is very necessary. A novel radar dwelling time control strategy based on an interacting multiple model algorithm is presented in this paper, which controls the dwelling time of radar according to predicted covariance matrix during tracking, taking advantage of the relation model between the dwelling time and the tracking performance. First, the complex radar network is built for target tracking. Secondly, the influence of the dwelling time is considered in the tracking performance of the complex radar network. Finally, a decision will be made after the dwelling time for every radar is obtained by particle swarm optimization, the radar with the smallest dwelling time will be selected to track target. The tracking accuracy and LPI performance are demonstrated in the Monte Carlo simulations. The results are validated through the comparison with other methods.
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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 | 0.002 | 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 it