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Record W1989097365 · doi:10.1109/icassp.2002.5745285

On uniqueness of direction of arrival estimates using RAnk Reduction Estimator (RARE)

2002· article· en· W1989097365 on OpenAlex
Marius Pesavento, Alex B. Gershman, K.M. Wong

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

VenueIEEE International Conference on Acoustics Speech and Signal Processing · 2002
Typearticle
Languageen
FieldComputer Science
TopicDirection-of-Arrival Estimation Techniques
Canadian institutionsMcMaster University
Fundersnot available
KeywordsUniquenessIdentifiabilityDirection of arrivalEstimatorRank (graph theory)Reduction (mathematics)Equivalence (formal languages)AlgorithmSIGNAL (programming language)MathematicsComputer scienceManifold (fluid mechanics)Applied mathematicsStatisticsCombinatoricsMathematical analysisTelecommunicationsEngineeringDiscrete mathematicsGeometry

Abstract

fetched live from OpenAlex

We study the uniqueness of the signal Direction Of Arrival (DOA) estimates obtained using the RAnk Reduction Estimator (RARE) [I] in partly calibrated subarray-based sensor arrays. A new identifiability condition is derived for such class of arrays which guarantees that the array manifold is unambiguous. The equivalence of the MUSIC solution for the signal DOA's (obtained in the fully calibrated array case) and the RARE solution (obtained in the case of partly calibrated array of the same configuration) is proved and the uniqueness of the RARE DOA estimates is established.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.675
Threshold uncertainty score0.726

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 head0.059
GPT teacher head0.317
Teacher spread0.257 · 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