Analysis of discriminants for experimental 3D SAR imagery of human targets
Bibliographic record
Abstract
Development of a prototype 3-D through-wall synthetic aperture radar (SAR) system is currently underway at Defence Research and Development Canada. The intent is to map out building wall layouts and to detect targets of interest and their location behind walls such as humans, arms caches, and furniture. This situational awareness capability can be invaluable to the military working in an urban environment. Tools and algorithms are being developed to exploit the resulting 3-D imagery. Current work involves analyzing signatures of targets behind a wall and understanding the clutter and multipath signals in a room of interest. In this paper, a comprehensive study of 3-D human target signature metrics in free space is presented. The aim is to identify features for discrimination of the human target from other targets. Targets used in this investigation include a human standing, a human standing with arms stretched out, a chair, a table, and a metallic plate. Several features were investigated as potential discriminants and five which were identified as good candidates are presented in this paper. Based on this study, no single feature could be used to fully discriminate the human targets from all others. A combination of at least two different features is required to achieve this.
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".