Microwave Enhanced IR Detection of Landmines Using 915 MHz and 2450 MHz
Bibliographic record
Abstract
As a continuation of previous studies in microwave enhanced infrared (IR) detection of landmines at DRDC Ottawa, additional experiments were performed using a microwave source at 2450 MHz to illuminate buried inert antipersonnel and antitank landmines. Further experiments were performed for the first time using a microwave device at 915 MHz with an open waveguide. Infrared detection was accomplished using a FLIR A20M IR camera in the 8-12 mm region. An investigation of this method was done by examining mine signatures made up of two components: the microwave interference on the surface of the sand caused by the superposition of incident and reflected microwave beams, and the microwave absorption by the mine and sand causing a temperature difference to be thermally conducted to the surface of the soil. Results are presented for a wide variety of experimental arrangements. An attempt at simulating various minefield conditions was explored. Some of the surfaces examined above buried mine targets include smooth, moist, hand brushed, very uneven, and raked soil. Introducing clutter on the surface of the soil such as pebbles, rocks, leaves and wood has also been studied. Many of these parameters impeded the detection process. Thus, this method is not an allencompassing solution to mine detection but may improve IR methods in circumstances such as dark or cloudy weather. Outlined recommendations concerning future scientific studies into the proposed method may refine microwave enhanced IR imagery.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".