Bibliographic record
Abstract
This paper studies the capability of ultra-wideband short-pulse (UWB SP) radar to provide surveillance through concrete walls, including multistatic radar surveillance and 3D through-wall imaging. A full-wave electromagnetic simulator is used to generate high fidelity through-wall radar data. The raw radar data are transformed into radar images using a back projection algorithm. It is shown that UWB SP radar can track targets moving inside a room with concrete walls as well as providing static mapping of the room interior. The velocity of the electromagnetic wave inside a concrete wall is reduced compared to free space thus defocusing target images, displacing targets from their true positions, and possibly producing false targets. This problem can be mitigated by including the time of flight difference due to the concrete walls into the image generation algorithm. 3D through-wall radar imaging obtained using UWB SP radar centered at 2 GHz requires a very large antenna aperture to be able to see the shape of the human phantoms. However, using a center frequency of 10 GHz reduces this aperture requirement fivefold making the system more practical operationally. However, attenuation is much higher at 10 GHz than at 2 GHz.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 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".