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Record W1994419003 · doi:10.1109/taes.2012.6324673

Effects of Mutual Coupling on the Accuracy of Adcock Direction Finding Systems

2012· article· en· W1994419003 on OpenAlexaff
Simon Henault, Yahia M. M. Antar, Sreeraman Rajan, Robert Inkol, Sichun Wang

Bibliographic record

VenueIEEE Transactions on Aerospace and Electronic Systems · 2012
Typearticle
Languageen
FieldComputer Science
TopicDirection-of-Arrival Estimation Techniques
Canadian institutionsDefence Research and Development CanadaRoyal Military College of Canada
Fundersnot available
KeywordsAntenna (radio)Coupling (piping)Direction findingElectronic engineeringComputer scienceRange (aeronautics)Key (lock)Cover (algebra)ImplementationAntenna arrayTopology (electrical circuits)EngineeringElectrical engineeringTelecommunicationsAerospace engineering

Abstract

fetched live from OpenAlex

The design of direction finding (DF) systems intended to operate over large frequency ranges involves various compromises. Typical implementations involve the use of multiple antenna arrays, each of which is scaled to cover a portion of the frequency range of interest. To minimize the size of the resulting multiband antenna assembly, two or more arrays can be combined in a coplanar antenna configuration. However, this introduces the issue of mutual coupling between the elements of the different arrays. A comprehensive study of the effects of mutual coupling on the estimation error for this type of antenna array configuration for two commonly used DF techniques is provided. Several important results clarify key design issues and trade-offs. It is shown that proper design choices are required to avoid uncorrectable ambiguities and the resulting large estimation errors.

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)0.001
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: none
Teacher disagreement score0.721
Threshold uncertainty score0.551

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.014
GPT teacher head0.251
Teacher spread0.237 · 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

Citations11
Published2012
Admission routes1
Has abstractyes

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