Aircraft identification from RCS measurement using an orthogonal transform
Why this work is in the frame
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Bibliographic record
Abstract
A comparative study on target identification using the radar cross section (RCS) signature of an aircraft in both the frequency domain and the range domain is conducted. A maximum likelihood method is employed to perform the identification process. Generalised likelihood identification when the received RCS signal is attenuated by an unknown amount is also examined. Target identification could be quite computationally intensive since a large number of library reference signatures may have to be searched to declare an identification. The use of an orthogonal transform is proposed to reduce the computational requirement. It is found that the discrete cosine transform is very effective in compacting the RCS signature in the frequency domain, and the Haar transform is more efficient in the range domain. The application of orthogonal transforms can reduce the computational complexity by at least 50% while maintaining the same identification accuracy.
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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 | 0.000 | 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 | 0.001 |
| 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