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Record W2374045324

Orbit Object ISAR Echo Signal Simulation

2006· article· en· W2374045324 on OpenAlexaff
Chao Liu, Atr Key

Bibliographic record

VenueJisuanji fangzhen · 2006
Typearticle
Languageen
FieldEngineering
TopicSpace Satellite Systems and Control
Canadian institutionsL'Alliance Boviteq
Fundersnot available
KeywordsEcho (communications protocol)RadarOrbit (dynamics)SIGNAL (programming language)Computer scienceInverse synthetic aperture radarComputer visionObject (grammar)Radar imagingAcousticsArtificial intelligencePhysicsEngineeringTelecommunicationsAerospace engineering
DOInot available

Abstract

fetched live from OpenAlex

The aim of this article is to study a method to simulate the ISAR radar echo of space objects, thus providing signal and data for the development of Space Target Detection Radar and the research of the characteristics of space targets. Firstly, the space object' s moving characteristics are researched, and a way to calculate the orbit of ballistic object is given, by which the target' s gesture can be fixed at any time, and the scatters' distribution of the target can be got. Then, the radar echo characteristics are researched under the irradiation of long - pulse - wide - band radar signal. With the distribution information of scatters provided by the orbit computation, the IF echo is simulated. At last, a method to realize the ISAR echo signal simulator based on orbit object is proposed, and the experimentation result is presented. Proved in the application, it's feasible to simulate the wide - band radar echo of space targets by this means.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.006
GPT teacher head0.202
Teacher spread0.196 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2006
Admission routes1
Has abstractyes

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