Bibliographic record
Abstract
The proliferation of laser-assisted weapons on the battlefield has prompted the development of laser warning receivers (LWR) to protect the platforms. Such devices are required to identify, locate and characterize the laser threats so that responsive countermeasures (CM) can be effectively deployed. The laser-assisted weapons can be divided in three main categories namely the laser rangefinders (LRF), the laser target designator (LTD) and the laser beam riders (LBR). The two first types are based on low-divergence high peak-power laser sources whereas the LBRs use a variable divergence low-power source. The problem for a LWR to detect these lasers comes from the huge dynamic range (9 decades) necessary to both detect the lasers on-axis and off-axis up to a few degrees. Moreover, in the case of the LBR, the detection threshold has to be set extremely low to cope with the very low irradiance it generates at the LWR. Normally a separate detection channel is necessary for the LBR and the angular resolution very limited. This paper describes the laser threats and the phenomenology involved in the detection process. The work done at DRDC Valcartier in the domain of laser sensors and LWRs is presented together with a series of results obtained in the field. Finally, the CM aspect and the integration of the LWR into a more complete protection suite are discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".