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Record W2165555792 · doi:10.1109/vetecs.2005.1543241

High Resolution Estimation of Directions of Arrival

2005· article· en· W2165555792 on OpenAlexaff
Tolga Kurt, Abbas Yongaçoğlu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicDirection-of-Arrival Estimation Techniques
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMatching pursuitA priori and a posterioriComputer scienceConvergence (economics)AlgorithmDirection of arrivalMatching (statistics)EstimationResolution (logic)Tree (set theory)High resolutionArtificial intelligenceMathematicsEngineeringStatisticsTelecommunicationsRemote sensingGeographyCompressed sensing

Abstract

fetched live from OpenAlex

In this paper, we propose a novel system architecture that employs the flexible tree search based orthogonal matching pursuit (FTB-OMP) algorithm for direction of arrival estimation. It is shown by simulation results that the FTB-OMP algorithm has high estimation performance and resolution capability when compared to the MUSIC and OMP algorithms. The proposed system does not require a priori knowledge on the number of directions to be resolved and uses very small amount of snapshots for convergence. It is also superior to the ESPRIT algorithm since ESPRIT cannot resolve angles that are separated more than a threshold.

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.609
Threshold uncertainty score0.274

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.012
GPT teacher head0.265
Teacher spread0.253 · 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
GenreMethods

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

Citations4
Published2005
Admission routes1
Has abstractyes

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