Concept of operation and preliminary experimental results of the DRDC through-wall SAR system
Bibliographic record
Abstract
Mapping the interior of buildings is of great interest to military forces operating in an urban battlefield. Throughwall radars have the potential of mapping interior room layout, including the location of walls, doors and furniture. They could provide information on the in-wall structure, and detect objects of interest concealed in buildings, such as persons and arms caches. We are proposing to provide further context to the end user by fusing the radar data with LIDAR (Light Detection and Ranging) images of the building exterior. In this paper, we present our system concept of operation, which involves a vehicle driven along a path in front of a building of interest. The vehicle is equipped with both radar and LIDAR systems, as well as a motion compensation unit. We describe our ultra wideband through-wall L-band radar system which uses stretch processing techniques to obtain high range resolution, and synthetic aperture radar (SAR) techniques to achieve good azimuth resolution. We demonstrate its current 2-D capabilities with experimental data, and discuss the current progress in using array processing in elevation to provide a 3-D image. Finally, we show preliminary data fusion of SAR and LIDAR data.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".