MétaCan
Menu
Back to cohort
Record W1522978537 · doi:10.1109/icassp.1989.267002

Direction of arrival estimation in the presence of noise with unknown, arbitrary covariance matrices

2003· article· en· W1522978537 on OpenAlex

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

VenueInternational Conference on Acoustics, Speech, and Signal Processing · 2003
Typearticle
Languageen
FieldComputer Science
TopicDirection-of-Arrival Estimation Techniques
Canadian institutionsUniversity of WaterlooMcMaster University
Fundersnot available
KeywordsDirection of arrivalEstimatorCovariance matrixNoise (video)AlgorithmCovarianceProbability density functionCovariance functionMathematicsSigmaComputer scienceFunction (biology)Matrix (chemical analysis)Applied mathematicsStatisticsPhysicsArtificial intelligenceTelecommunications

Abstract

fetched live from OpenAlex

A novel method for estimating directions of arrival of plane waves impinging on arrays of sensors is proposed. The method is particularly well suited t the case in which the background noise field is nonisotropic, with arbitrary covariance matrix. The joint posterior probability density function of the signal parameters and the noise covariance matrix Sigma is formed, and then the dependence on Sigma is integrated out, after a suitable noninformative prior p( Sigma ) is defined. The resulting estimator structure is then modified to reduce the computational requirements substantially. Significantly improved performance over the MUSIC algorithm, particularly with regard to threshold, is observed.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.846
Threshold uncertainty score0.381

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 communication0.0000.001
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 head0.023
GPT teacher head0.282
Teacher spread0.260 · 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