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Record W4416155286 · doi:10.1063/5.0292890

The imaging algorithm in the optimal range FrFT domain based on Doppler parameters

2025· article· en· W4416155286 on OpenAlexaboutno aff
Zhenli Wang, Yulong Xu, J. Y. Liu, Guangliang Gao

Bibliographic record

VenueAIP Advances · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced SAR Imaging Techniques
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsDoppler effectEnergy (signal processing)Range (aeronautics)Fourier transformRotation (mathematics)Frequency domainEstimation theory

Abstract

fetched live from OpenAlex

In view of the error impact of Doppler parameters estimation on SAR imaging performance, this paper constructs the energy focusing axis based on the optimal rotation angle in the optimal range FrFT (fractional Fourier transform) domain. On this basis, the relationship among Doppler center frequency, Doppler FM (frequency modulation) rate, and energy focusing axis, as well as the relationship between Doppler center frequency and Doppler FM rate are derived in detail, and then, the basic framework of the D-FrFT-RD algorithm was constructed. The imaging test of Canadian spaceborne RADARSAT-1 measured data shows that when the error of Doppler center frequency estimation gradually increases from 0% to 5%, the energy focusing axis curve may deviate from the normalized value to some extent, but in reality, the impact on SAR imaging performance parameters is not significant. Compared to the traditional RD (range Doppler) algorithm, the energy focusing axis curve corresponding to the D-FrFT-RD algorithm in this paper is relatively stable, with high range resolution, and varying degrees of improvement in both range PSLR and range ISLR.

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 score0.805
Threshold uncertainty score0.484

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 head0.005
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

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

Citations0
Published2025
Admission routes1
Has abstractyes

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