Position and attribute fusion of radar, ESM, IFF and Datalink for AAW missions of the Canadian Patrol Frigate
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it