Target tracking and identification issues when using real data
Bibliographic record
Abstract
Since 1991, the Research and Development (R&D) group at Lockheed Martin Canada (LM Canada) has been developing and demonstrating the application of Multi-Source Data Fusion (MSDF) techniques for target tracking and identification within the Naval Command and Control (C2) for the HALIFAX Class frigates. The current C2 as well as the sensor suite of the HALIFAX Class were designed in the early 80s and based around a proprietary hardware architecture. The sensor data is pre-processed and provided to the C2 in real time. Considering the late 70s and 80s design of the sensor interfaces, where a data fusion within the C2 was not a commonality, not all of the information beneficial for a data fusion system is provided to the C2. After a sequence of simulation and modelling efforts for an MSDF capability within the HALIFAX Class C2, this project is now at a point where real data captured from a ship trial on the HALIFAX Class is being injected into the MSDF. This is an ongoing activity and a number of iterations are foreseen before the MSDF becomes part of HALIFAX Class C2. This paper provides a summary of lessons learned in this exercise.
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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 | 0.016 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".