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Record W1986587640 · doi:10.1109/tgrs.2012.2184798

Detection and Estimation With RADARSAT-2 Moving-Object Detection Experiment Modes

2012· article· en· W1986587640 on OpenAlexaboutno aff
Marina V. Dragosevic, Wesley Burwash, Shen Chiu

Bibliographic record

VenueIEEE Transactions on Geoscience and Remote Sensing · 2012
Typearticle
Languageen
FieldEngineering
TopicAdvanced SAR Imaging Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsMoving target indicationSynthetic aperture radarComputer scienceObject detectionRemote sensingComputer visionInverse synthetic aperture radarPhase centerArtificial intelligencePosition (finance)Antenna (radio)Radar imagingRadarReal-time computingContinuous-wave radarTelecommunicationsGeologyPattern recognition (psychology)

Abstract

fetched live from OpenAlex

This paper describes in detail several multichannel variants of the famous displaced phase center antenna (DPCA) method suitable for spaceborne multichannel synthetic aperture radar ground moving target indication (GMTI), which has become available through the RADARSAT-2 moving-object detection experiment (MODEX). A flexible signal processing architecture was developed at Defense Research and Development Canada to support R&D work in GMTI and to provide a tool for MODEX system validation. The presented multichannel DPCA method is one of the first algorithms that have been implemented and successfully tested using this architecture. The main objective is to provides a simple, yet effective and robust way to detect ground moving vehicles and to estimate their position and velocity. This work forms a baseline for further advancement in the GMTI area.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.010
GPT teacher head0.234
Teacher spread0.224 · 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

Citations64
Published2012
Admission routes1
Has abstractyes

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