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Record W2143224046 · doi:10.1109/lgrs.2013.2293475

Motion Parameter Estimation and Focusing From SAR Images Based on Sparse Reconstruction

2014· article· en· W2143224046 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.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueIEEE Geoscience and Remote Sensing Letters · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced SAR Imaging Techniques
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSynthetic aperture radarComputer scienceComputer visionArtificial intelligenceBasis (linear algebra)BeamwidthInverse synthetic aperture radarRadar imagingMotion estimationIterative reconstructionMatching pursuitPosition (finance)Motion (physics)RadarMathematicsCompressed sensing

Abstract

fetched live from OpenAlex

This letter presents a new motion parameter estimation method using synthetic aperture radar (SAR) images based on sparse reconstruction. The method uses orthogonal matching pursuit, which correlates a SAR image with elements in a reference basis. The reconstruction result provides a high-resolution focused image and corresponding motion parameters. It does not require narrow-beamwidth assumption and prior motion information, as compared with the existing method based on matching pursuit. We design the reference basis by theoretically analyzing the performance degradation due to parameter mismatch. We further calculate required position and velocity parameter resolutions in the basis. The calculated resolutions are validated by means of simulations. Imaging examples with real SAR images of a scene acquired over Ottawa, Canada, show the effectiveness of our method.

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)0.000
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 score0.916
Threshold uncertainty score0.512

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 head0.009
GPT teacher head0.213
Teacher spread0.204 · 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