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Record W2217162478

A hybrid SAR processing method for data collected using a stationary bistatic receiver

2008· article· en· W2217162478 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

VenueSynthetic Aperture Radar (EUSAR), 2008 7th European Conference on · 2008
Typearticle
Languageen
FieldEngineering
TopicAdvanced SAR Imaging Techniques
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsBistatic radarComputer scienceSignal processingAlgorithmConvolution (computer science)TransmitterResidualFilter (signal processing)Constant (computer programming)RadarArtificial intelligenceRadar imagingTelecommunicationsComputer visionChannel (broadcasting)Artificial neural network
DOInot available

Abstract

fetched live from OpenAlex

This paper introduces a method to process bistatic data collected in a configuration where the receiver is stationary and the transmitter has a constant velocity and constant altitude. The signal processing uses a standard Fourier-based convolution to achieve the bulk of the SAR processing followed by fast, timevarying filtering to correct residual e A simulation is used to compare the proposed method with the method of non-linear chirp scaling.rrors. Theoretical analysis and simulated data are used to demonstrate the approach.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.871
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.077
GPT teacher head0.309
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