Novel architecture for a hybrid acoustic-radar buried-object detection system
Bibliographic record
Abstract
We have created a new architecture for the detection and location of specific buried targets. The system uses a combination of acoustic vibrations and electromagnetic waves to achieve highly specific target recognition, and a multistatic configuration to determine target location. The mechanical vibration resonance properties of the constituent elements of the targets constitute a signature which can be identified in clutter. In order to better detect these vibrations, continuous-wave radar signals are used rather than acoustic reflections, as in sonar-based systems. The energy stored in resonant vibrating elements is not directly detected, but rather modulates the radar signal. The received signals are sampled at high resolution to facilitate target signature recognition by cross-correlation and phase measurement. Location is accomplished by travel time determination for each receiver using absolute phase measurements at multiple frequencies. The phase measurements provide multiple sets of confocal elliptical lines of position, whose intersection identifies the target location. The region in which a mine can be precisely located is a subset of the region in which its presence can be detected.
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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 | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".