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Record W4402306982 · doi:10.18280/ts.410427

A Repeater Deception Jamming System Based on High Gain Antenna Array Spatial Separation Receiving

2024· article· en· W4402306982 on OpenAlexvenueno aff
Zhichun Dai, Ding Pan, Peng Wu, Lanxia Xu, Jing Li

Bibliographic record

VenueTraitement du signal · 2024
Typearticle
Languageen
FieldEngineering
TopicFull-Duplex Wireless Communications
Canadian institutionsnot available
FundersChang'an University
KeywordsRepeater (horology)JammingAntenna (radio)Antenna arrayDeceptionSeparation (statistics)Array gainComputer scienceElectronic engineeringTelecommunicationsEngineeringPhysicsPsychologyArtificial intelligenceSocial psychology

Abstract

fetched live from OpenAlex

Aiming at the problems of high cost and high-power consumption of the existing repeater satellite navigation deception jamming system, a repeater deception jamming system based on high gain antenna array spatial separation reception is designed.Through eight sets of high-gain parabolic antennas, the appropriate satellite is selected to obtain a single satellite signal, and then the time delay of each satellite signal is accurately controlled according to the spoofing position point, and the pseudo-range information is changed.Finally, the target receiver is transmitted to the target receiver in a combined way to achieve the purpose of deception.At the same time, in order to ensure the effectiveness of the interference, combined with the satellite space geometry, the optimal satellite strategy algorithm is designed.Field experiments show that the system can successfully deceive typical receivers.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.012
GPT teacher head0.228
Teacher spread0.216 · 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 designBench or experimental
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

Citations3
Published2024
Admission routes1
Has abstractyes

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