Performance evaluation of ESM deinterleaver using TOA analysis
Bibliographic record
Abstract
A radar electronic support measures (ESM) system performs the functions of threat detection and area surveillance to determine the identity of surrounding emitters. ESM systems incorporate a passive receiver that measures the parameters of the detected radar pulses and a deinterleaver processor that sorts and segregates the received radar pulses into a number of radar cells depending on the mono-pulse parameters of the received pulses. These radar cells are submitted to the threat library of the EW system and compared with stored parameters of known radars to determine the identity of the estimated radar cell and take appropriate action against it. If there is no match with any radar in the threat library, the library is updated to include this new intercepted radar. However, if the deinterleaver does not work properly, false radars are generated. These false radars force the EW system to use military resources against false threats. This paper presents a method for evaluating the performance of the deinterleaving algorithms based on the TOA information inside the estimated radar cells. Thus, if some of the estimated radar cells achieve a certain confidence level, only these cells are submitted to the threat library. This method is useful in avoiding wasting limited resources, and can be applied to stable, jitter, and stagger PRI radars.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".