Classification of Radar Targets via Distribution Matching of Late-Time Resonance Parameters
Why this work is in the frame
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Bibliographic record
Abstract
A promising non-imagining approach to the classification of radar targets is to use the frequencies and attenuation rates of the resonant modes that present during a target’s late-time response (LTR) as features. Unfortunately, the estimation of these resonance parameters is rather sensitive to noise. However, we observe that when a large number of measurements of the LTR can be taken in a short time, the probability distribution of the estimates of the parameters can be estimated, and then matched against a database of such distributions. That has the potential to reduce the sensitivity of the classification problem to noise. In this paper, we develop a pragmatic approach to target classification using this distribution-matching approach, and demonstrate its effectiveness through physical experiments. The proposed approach is shown to be highly robust to environmental clutter and somewhat robust to target orientation.
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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 | 0.000 |
| 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