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Record W2356196513

Study on Selection of the Antenna Array in Compass Navigation System

2015· article· en· W2356196513 on OpenAlexaff
Liu Bao-gu

Bibliographic record

VenueXinxi wangluo anquan · 2015
Typearticle
Languageen
FieldEngineering
TopicInertial Sensor and Navigation
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsComputer scienceCompassAntenna (radio)Interference (communication)TelecommunicationsTime domainGNSS applicationsFrequency domainAntenna arrayElectronic engineeringGlobal Positioning SystemComputer visionEngineeringPhysics
DOInot available

Abstract

fetched live from OpenAlex

Compass navigation system has increasingly played more and more important roles in the military and civil navigation. Although the signal of compass using a spread spectrum technique makes it own a certain degree of interference margin, it still can be affected sensitively by interference due to its weak signal power. The diversity of interference makes anti-jamming difficulty by just using either time domain filtering technology or space domain filtering technology. Among all interferences, broadband interference is most influential and most difficult to overcome. To solve these problems, we use Space-Time adaptive processing technique in this article and it is more useful comparing with time domain filtering technology or space domain filtering technology or frequency domain filtering technology as it increases the degrees of freedom without increasing the number of antenna. However different antenna array has different infl uence using Space-Time adaptive processing technique. So in this article we have discussed the space-time responses of different antenna array and improvement factor of each antenna array. The simulation shows that circular antenna array plays better in antijamming process. It could provide a theoretical foundation in the design of Bei Dou receiver.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.137
Threshold uncertainty score0.288

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 head0.026
GPT teacher head0.245
Teacher spread0.219 · 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 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

Citations0
Published2015
Admission routes1
Has abstractyes

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