Using A Priori Databases for Identity Estimation through Evidential Reasoning in Realistic Scenarios
Bibliographic record
Abstract
Canadian defence companies and Government Research and Development (R&D) laboratories have long ago recognized the necessity to develop comprehensive a priori databases containing all the possible attributes that can be inferred by measurements coming from a given sensor suite. In order to maintain this document at a NATO unclassified level, a small portion of an existing (consisting of more than 2200 platforms) database is presented, which nevertheless contains all the salient features needed for refining the identity (ID) of any target by the fusion of sensor information. In addition, only the information gathered from unclassified sources such as Jane's and Periscope is presented. This a priori Platform DataBase (PDB) contains all the possible naval and air targets, military or commercial, that can be encountered in realistic scenarios, and all the attributes that can be measured by any sensor belonging to any own-platform of the Canadian Forces (CF), ensuring its possible common use throughout the CF. Also presented and explained are all the attributes and all the correlations between platforms that are appropriate to Situation and Threat Assessment (STA or higher-level fusion), and which are present in the larger database. This paper focuses on only one own-platform of the CF in relevant scenarios, the maritime surveillance aircraft CP-140 Aurora (a Canadian version of the US's P3-C with S2-B avionics) in its present operational status, and also with an anticipated upgraded sensor suite. Validation and benchmarking of the chosen evidential reasoning scheme for identity estimation, is performed through several simulated scenarios that make use of DRDC-Valcartier Concept Analysis and Simulation Environment for Automatic Target Tracking and Identification (CASE-ATTI) sensor module.
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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 | 0.012 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".