Position and attribute fusion of radar, ESM, IFF and Datalink for AAW missions of the Canadian Patrol Frigate
Bibliographic record
Abstract
The R&D group at Lockheed Martin Electronic Systems Canada (LMESC) has now implemented the second version (v2) of its Data Fusion Demonstration Model (DFDM) for a naval anti-air warfare platform. This project has been designed to read data passively on the Canadian Patrol Frigate (CPF) bus without any modification to the CPF software. DFDM v2 has the capability to fuse data from the following CPF sensors: 2 surveillance radar, 2 slaved identification friend or foe, an electronics support measure, the communication intercept operator and a tactical data link (Link-II). The fusion of data from non-organic sensors with the tactical Link-II data has produced spatial alignment problems which have been overcome by the use of a geodetic referencing coordinate system. A new Kalman filter with adaptive process noise provides significantly improved tracking capabilities. Two enhancements have been implemented into a Dempster-Shafer evidential reasoning over attribute data: the addition of pruning rules to reduce the set of identity propositions, and the use of fuzzy logic for confidence level distribution.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".