Methodology to determine window length for unknown target detection in electronic warfare system
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
To detect threat signals in electronic warfare support systems, a detector that uses a plurality of windows with various sizes should be designed such that the length of all the signal sources can be considered. Since a large number of these windows cause excessive computational complexity, the number of windows of the detector is reduced by using a small number of representative windows. In this case, since a window is dedicated to the unknown signal of a certain interval, deterioration of the detection performance is inevitable owing to the inconsistency between the lengths of the received signal and the window size. Hence, the deterioration of the detection performance should be minimised by analysing the relation between the lengths of a window and a signal. However, the conventional analysis methods of detection performance are not suitable because they are based on the premise that the lengths of the signal and window are consistent with each other. The authors propose a novel analysis method using processing gain to overcome this limitation, which can be applied irrespective of the inconsistency between the lengths of a window and a signal. Based on this analysis, they present a method to obtain an optimal window length that minimises degradation of the detection performance and subsequently verify the result using simulation.
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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 | 0.001 | 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