Experimental 3-D SAR human target signature analysis
Bibliographic record
Abstract
Defence Research and Development Canada has been investigating 3-D through wall synthetic aperture radar (SAR) imaging from an experimental L-band through-wall SAR prototype. Tools and algorithms for 3-D visualization are being developed to exploit the resulting imagery. In this paper, a comprehensive study of the characteristics of human target signatures in free space and behind two different wall structures is presented using 3-D SAR data. The aim of this investigation is to gain a better appreciation of the signatures of targets when placed behind different wall materials. An analysis of the human target signature in different poses is provided. There was very close agreement between the measured physical dimensions of the targets and those obtained from the strong returns in the SAR imagery. Viewing of the SAR data as 2-D slices provides a qualitative means of discriminating between different target signatures. A more useful approach to discrimination is to quantify these differences. The next phase of this investigation will look at different quantitative features as potential discriminants.
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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 | 0.000 | 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) | 0.004 | 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".