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Record W2386687784

An Imaging Technique Based on Distributed Multi-channel Radars

2007· article· en· W2386687784 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

VenueJournal of Electronics Information & Technology · 2007
Typearticle
Languageen
FieldEngineering
TopicAdvanced SAR Imaging Techniques
Canadian institutionsL'Alliance Boviteq
Fundersnot available
KeywordsComputer scienceRadarInverse synthetic aperture radarChannel (broadcasting)Radar imagingEcho (communications protocol)Computer visionMotion compensationRemote sensingArtificial intelligenceGeologyTelecommunications
DOInot available

Abstract

fetched live from OpenAlex

This paper proposes an non—cooperative target imaging technique based on the distributed multi-channel radar system,which consists of multiple sparsely—located transmitters and receivers.By coherently processing the signals received in multiple radar channels,the wave-number samples in a plane are obtained,from which the two-dimensional reflectivity function of target is reconstructed.Due to the simultaneous formation of multi-channel echo data and coherent processing of a singlesnap,it does not require large antennas,and the complicated motion compensation performed in ISAR imaging is not needed.Meanwhile,the reconstructed images are naturally scaled because the sampling locations in wave-number space are known.To validate the imaging performance,the distributed radar systems with wide-band transmitted signal are designed respectively,and the sireulations are demonstrated.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.873
Threshold uncertainty score0.806

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.005
GPT teacher head0.252
Teacher spread0.247 · 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