Simulator for advanced fighter radar EPM development
Bibliographic record
Abstract
In order to develop electronic protection measures (EPM) for airborne intercept radars, it is necessary to fully understand the often complex effects of different types of electronic countermeasure (ECM) techniques on the radar operation. To aid in this effort, a software-based high-fidelity simulator called SAFIRE has been developed. SAFIRE models target clutter and ECM signal returns on a pulse-by-pulse basis for arbitrarily long scenario durations. Conventional air-to-air processing modes that are typically used in airborne intercept radars for search and track functions are also modelled. The capabilities of SAFIRE are described, with particular emphasis on the ECM model. The ECM model is based on a modern coherent jammer which uses digital radio frequency memory (DRFM) technology to generate false targets and ‘smart’ noise for sophisticated ECM techniques. Sample results from SAFIRE are presented to illustrate its utility in analysing the effects of such ECM techniques on the radar processing. Benchmark timing results for SAFIRE code execution are also provided, which indicate that for many jamming scenarios of interest, the code execution is only about five times slower than real time. This is a significant feature since the scenario durations that are required for these studies are often relatively long.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".