Queueing analysis of the deinterleaving of radar pulses in a dense emitter environment
Bibliographic record
Abstract
An ESM system consists of a passive radar receiver that receives pulses from the surrounding radars and measures their monopulse parameters, and a deinterleaver that sorts the digital words representing the parameters of theses pulses and groups them in individual radar cells. The parameters of the deinterleaved radar cells are then compared owith those of known radars to identify the intercepted radars. The high pulse rate and the low processing speed of both the ESM receiver and the deinterleaver may lead to a large number of missing pulses from the radar cells which consequently results in errors in the deinterleaving process. In this paper, we derive an expression for the factor of successful processing of the ESM system F/sub s/ as a function of the pulse rate and the parameters of the ESM system. For the given ESM parameters, and the minimum acceptable value of F/sub s/ we can determine the highest pulse rate that allows the ESM system to work properly. Inversely, if the pulse rate is known or measured, the performance of the ESM system can be predicted for the given parameters of the ESM system. All derived expressions are supported by extensive computer simulations.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".