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Record W4412779114 · doi:10.26866/jees.2025.4.r.302

Performance Comparison between Monostatic and Bistatic Staring Spotlight Modes Based on Several Scenarios

2025· article· en· W4412779114 on OpenAlexaff
Seong Joo Maeng, Suk-Jin Kim, Jung-Hwan Lim, Jae W. Lee, Taek-Kyung Lee, Woo-Kyung Lee

Bibliographic record

VenueJournal of Electromagnetic Engineering and Science · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced SAR Imaging Techniques
Canadian institutionsNexen (Canada)
FundersAgency for Defense DevelopmentNational Research Foundation of Korea
KeywordsBistatic radarStaringComputer scienceRemote sensingAcousticsPhysicsGeologyOpticsTelecommunicationsRadar

Abstract

fetched live from OpenAlex

This paper analyzes the performance of the bistatic staring spotlight mode in a low-orbit satellite environment and then compares it with the performance of the monostatic staring spotlight mode. The staring spotlight mode provides high-resolution images by continuously stare at the target via azimuth beam steering. However, monostatic synthetic aperture radar (SAR) systems may not achieve the desired performance in this mode owing to the geometric limitations of performing missions using a single satellite. To overcome these limitations, a bistatic SAR system that uses two satellites is considered in this study. By using two satellites, uncertain ground structures can be handled with flexibility. Moreover, a strategically planned design for a bistatic SAR system can help achieve a wider range and better performance than a monostatic SAR system. By applying this design to the staring spotlight mode, a SAR system that offers both high-resolution images and good performance is proposed.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.138
Threshold uncertainty score0.578

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.233
Teacher spread0.227 · 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 designSimulation or modeling
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

Citations0
Published2025
Admission routes1
Has abstractyes

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