MétaCan
Menu
Back to cohort

Surveillance RADAR development using frequency diversity to mitigate range eclipsing

2019· article· en· W3021281145 on OpenAlexaff
Jun Hyun Park, Jae Wook Park, Ji Hoon Ahn, Jong suk Yoon, Jong hwan Shin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRadar Systems and Signal Processing
Canadian institutionsSR Research (Canada)
Fundersnot available
KeywordsRadarWaveformRemote sensingContinuous-wave radarComputer scienceTransceiverFire-control radarDiversity schemeRadar lock-onPulse-Doppler radarRadar engineering detailsSIGNAL (programming language)3D radarBistatic radarRadar systemsLow probability of intercept radarInterference (communication)Radar imagingTelecommunicationsFadingGeographyWireless

Abstract

fetched live from OpenAlex

A coastal surveillance RADAR was designed to monitor coastal area for detection of air and sea targets. Waveform of RADAR was optimally designed to use long and short pulses in order to efficiently detect targets appeared long and short distance from the RADAR. Transceiver using frequency diversity was designed to mitigate range eclipsing. Signal processing was appropriately performed based on the waveform design. This study investigated the results of processing received signal from the developed RADAR system and verified the results based on real targets as the reference. Targets were detected without frequency interference between long and short pulses.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.016
GPT teacher head0.201
Teacher spread0.185 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

Same topicRadar Systems and Signal ProcessingFrench-language works237,207