Methodology to determine window length for unknown target detection in electronic warfare system
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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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.001 |
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".