Impact of the Tactical Picture Quality on the Fire Control Radar Search-Lock-On Time
Bibliographic record
Abstract
Abstract : Data fusion is suitable for a broad range of decision support applications. To cope with a larger class of problems and contexts, data fusion gains to be adaptive. Adaptation in data fusion corresponds to Level 4 of the JDL model, also referred to as process refinement. The Decision Support Systems Section (DSS) at Defence Research & Development Canada (DRDC)-Valcartier has initiated research activities aiming at developing and demonstrating advanced concepts of adaptive data fusion that could apply to the current Halifax and Iroquois Class Command & Control Systems (CCS), as well as their possible future upgrades, in order to improve their performance against the predicted future threats. This document gives a brief description of the adaptive data fusion concepts. It also presents a new Measure Of Effectiveness (MOE) that serves as an adaptation trigger in the target-tracking problem in maritime Above Water Warfare (AWW) applications. The proposed MOE uses the search to lock-on time of the Fire Control Radar (FCR) and aims at establishing and quantifying the effect of the quality of the Maritime Tactical Picture (MTP) on the diminution of battle space size and reaction time. Besides adaptation of the sensing and processing operation, this MOE allows addressing the trade-off between the time dedicated to the tracking with surveillance radars versus the time spent in search and lock-on with FCR.
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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 | 0.002 | 0.011 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".