Bibliographic record
Abstract
This report describes the DRDC Ottawa research activities and major findings on through-the-wall surveillance, using ultra-wideband (UWB) short-pulse (SP) radars. These activities include both experiments and simulations. Off-the-shelf UWB radio frequency (RF) equipment was purchased to support experimental investigations. For simulations, a 3D computer model of a single room with a cubic, conducting target was developed. UWB radar located outside the room transmits short UWB pulses while the target is moved around the room in discrete steps. At the beginning of the section, we first show that motion detection is easy, since the radar echoes continuously change in time. However, simple motion detection does not provide enough information for most applications of interest. There is a clear requirement to measure the range and direction of the moving targets. Clutter from fixed objects interferes with the detection of moving targets. One way to suppress these fixed clutter is to use difference waveforms, obtained by subtracting echo waveforms from each other. The results of this report clearly show the detection of a moving target and suppression of fixed clutter. The next step is to determine the direction of the moving target. An antenna array combined with back-projection processing is used for that purpose. The simulated results clearly demonstrate that hidden targets can be tracked in both range and direction. These results have been confirmed experimentally.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".