Polarimetric bistatic GPR imaging and detection of landmines in the near field with the “Vampire Effect”
Bibliographic record
Abstract
GPR images invariably suffer from a bandwidth restriction due to the low pass characteristics of the air-ground interface and are usually not clear enough to differentiate between what is a landmine and what is harmless clutter. The presence of clutter in the subsurface therefore contributes to high false alarm rates when landmine detection schemes are attempted. A landmine detection algorithm based on the vampire signature has been proposed and has been tested to verify its validity. The method involves the construction of a spatial filter from polarimetric data in order to enhance objects that exhibit the vampire effect while suppressing objects that do not. So far, the method has several significant limitations, most notably that the position and orientation of the symmetric scatters is strictly confined to be directly below the SAR path and that the target symmetry planes must include both antennas as well as the scan path. In this paper, the effects of off-centered measurements whereby the antennas are not necessarily contained in the target symmetry plane are discussed.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".