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Study on distance and angle measurement for single-base-station UWB positioning system with circular antenna array

2013· article· en· W2021564694 on OpenAlexaff
Hao Zhang, Fei Guo, Tingting Lu, Lingwei Xu, Min Wang, T. Aaron Gulliver

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIndoor and Outdoor Localization Technologies
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsMultipath propagationAngle of arrivalAntenna (radio)Computer scienceAcousticsAntenna arrayTime of arrivalAmplitudeElectronic engineeringPhysicsOpticsTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

A single-base-station ultra-wideband (UWB) positioning system with a circular antenna array is given in this study. MUSIC algorithm and pulse amplitude measurement are employed to achieve accurate distance and angle measurement for precise positioning. Compared with the traditional correlation function algorithm in dense multipath environments, MUSIC algorithm has high multipath resolution capability and high noise immunity. It can get accurate values of time of arrival (TOA) by detecting multipath signals. Then the precise distance can be obtained. Since the angle of arrival (AOA) of the received signal is a variable of the antenna gain function of circular antenna array, we can simply measure the pulse amplitudes of the received signals to calculate AOA values. Simulation results show that MUSIC algorithm can detect the first path precisely in the indoor line-of-sight (LOS) environment, which improves the accuracy of the distance measurement. And the pulse amplitude measurement is also an effective way to achieve high precise angle measurement.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.196
Teacher spread0.174 · 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

Citations5
Published2013
Admission routes1
Has abstractyes

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