MétaCan
Menu
Back to cohort
Record W2215930535 · doi:10.1109/tgrs.2012.2195320

Adaptive CFAR for Space-Based Multichannel SAR–GMTI

2012· article· en· W2215930535 on OpenAlexaff
Ishuwa Sikaneta, Christoph H. Gierull

Bibliographic record

VenueIEEE Transactions on Geoscience and Remote Sensing · 2012
Typearticle
Languageen
FieldEngineering
TopicAdvanced SAR Imaging Techniques
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsMoving target indicationClutterConstant false alarm rateComputer scienceSynthetic aperture radarDetectorFalse alarmArtificial intelligenceChannel (broadcasting)Computer visionRadarRadar imagingTelecommunicationsContinuous-wave radar

Abstract

fetched live from OpenAlex

This paper studies the statistics of clutter data measured with a multichannel space-based synthetic aperture radar (SAR) for the purpose of ground moving target indication (GMTI) and presents an algorithm that implements an adaptive constant false-alarm rate (CFAR) detector. It discusses the differences between airborne and space-based SAR-GMTI clutter measurements and proposes how to develop the statistics of the latter from widely published results for the former. Based upon one of the differences, it proposes an adaptive CFAR detector and demonstrates detection using measured RADARSAT-2 data. The adaptive CFAR algorithm does not require a texture distribution for heterogeneous clutter.

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 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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.942
Threshold uncertainty score0.623

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 head0.023
GPT teacher head0.255
Teacher spread0.232 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreMethods

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

Citations5
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on Geoscience and Remote SensingSame topicAdvanced SAR Imaging TechniquesFrench-language works237,207