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Record W2390352589

A Method of ISAR Imaging for Missile with Fake Target Interference

2004· article· en· W2390352589 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicInfrared Target Detection Methodologies
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsMissileInverse synthetic aperture radarEnvelope (radar)Interference (communication)Computer visionEcho (communications protocol)Computer scienceSIGNAL (programming language)Artificial intelligenceDoppler effectProcess (computing)Phase (matter)Radar imagingRadarEngineeringPhysicsTelecommunications
DOInot available

Abstract

fetched live from OpenAlex

A process of ISAR imaging for missile with fake target interference presents more challenges than for a single target. In this paper, a method based on the Keystone transform is proposed for dealing with a multi target echo signal. This approach removes radial velocity for all targets by estimating information from narrow band echo signal. Then, a Keystone transform, which carries the inherence of closely relevant to the central frequency, is used to compensate the relative speed remained on missile at the equivalent central frequency and to efficiently implement envelope aligning for the desired target while the doppler frequency of missile is undersampled. Third step of the method separates the rough missile image from range doppler plane where targets do not overlap each other. The last step applies further phase compensation algorithm to the separated single targets echo signal to develop a final fine target image. The effectiveness of this algorithm is testified by processing of emulational data.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.455
Threshold uncertainty score0.375

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 head0.025
GPT teacher head0.283
Teacher spread0.258 · 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

Quick stats

Citations0
Published2004
Admission routes1
Has abstractyes

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