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Record W2137530412 · doi:10.1109/ccece.2003.1226310

Joint deinterleaving/recognition of radar pulses

2004· article· en· W2137530412 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Signal Modulation Classification
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsMonopulse radarRadarComputer scienceElectronic warfareRadar trackerJoint (building)Radar engineering detailsPulse-Doppler radarRadar configurations and typesElectronic engineeringRemote sensingRadar imagingTelecommunicationsEngineeringGeology

Abstract

fetched live from OpenAlex

An electronic support measures (ESM) system consists of a passive radar receiver that receives and measures the monopulse parameters of pulses emitted by radars in its instantaneous view, and a deinterleaver that sorts these pulses and groups them into individual cells. The cell parameters are compared with those stored in the threat library of the electronic warfare (EW) system to identify the intercepted radars. This paper proposes a new approach to deinterleave the intercepted pulses and identify the corresponding radars in one step. The proposed approach can successfully identify radars whose angles of arrival are very close. Moreover, the proposed approach can be applied as an integral part of the adaptive deinterleaving algorithm to prevent the ESM from taking actions against false radars and consequently, avoids a waste of the available resources. Computer simulation results have shown that the proposed approach can successfully deinterleave radar pulses and identify the corresponding radars.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.739
Threshold uncertainty score0.221

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.050
GPT teacher head0.245
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it