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Record W2118904946 · doi:10.1109/eit.2008.4554297

Network localization using angle of arrival

2008· article· en· W2118904946 on OpenAlex
Yanping Zhu, Daqing Huang, Aimin Jiang

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
TopicIndoor and Outdoor Localization Technologies
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsAngle of arrivalRange (aeronautics)Quadratic equationComputer scienceAlgorithmQuadratic programmingTime of arrivalMathematical optimizationMathematicsGeometryTelecommunicationsEngineeringAerospace engineeringWireless

Abstract

fetched live from OpenAlex

In this paper, we propose two localization methods using angle of arrival (AoA) information. We assume that nodespsila axis orientations are unknown. Therefore, all AoA measurements are employed to calculate the angle differences of two different nodes viewed by the third one. Distance measurements between two nodes within the communication range are also utilized in the first method. For the second method, only AoA information is required. As all distance and angle measurements are accurate enough, the localization problem can be formulated as a linear program (LP). Otherwise, by introducing auxiliary variables, it can be cast as a quadratic program (QP). Simulation examples are presented to illustrate the effectiveness of the proposed methods.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.865
Threshold uncertainty score0.216

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 head0.022
GPT teacher head0.207
Teacher spread0.185 · 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