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Record W4390666312 · doi:10.3389/fmars.2023.1329898

Broadband high-resolution direction of arrival estimation using the generalized weighted Radon transform

2024· article· en· W4390666312 on OpenAlexaff
Mingyang Lü, Dajun Sun, T. Aaron Gulliver, Yunfei Lv, Jidan Mei

Bibliographic record

VenueFrontiers in Marine Science · 2024
Typearticle
Languageen
FieldComputer Science
TopicDirection-of-Arrival Estimation Techniques
Canadian institutionsUniversity of Victoria
FundersNational Natural Science Foundation of China
KeywordsAzimuthRobustness (evolution)AlgorithmRadon transformWavenumberComputer scienceDirection of arrivalBeamformingDeconvolutionBroadbandEnergy (signal processing)Frequency domainTime domainMathematicsTelecommunicationsStatisticsOpticsPhysicsAntenna (radio)Artificial intelligenceComputer vision

Abstract

fetched live from OpenAlex

Traditional direction of arrival (DOA) estimation algorithms typically have poor spatial resolution and robustness. In this paper, we propose a broadband high-resolution DOA estimation method based on the generalized weighted Radon transform (GWRT). The array signal can be converted into the frequency-wavenumber ( f-k ) domain using the conditional wavenumber spectrum function (CWSF). Then, a linear integral mathematical model for high-resolution DOA estimation is derived by transforming the f-k domain into the azimuth-energy domain using the GWRT. Computer simulation and sea trials were conducted to validate the feasibility and performance of the proposed method. The results obtained indicate that the proposed method yields a lower sidelobe level and can more effectively suppress the output energy in the non-target direction when compared to the conventional beamforming (CBF), steered minimum variance (STMV), and deconvolution (DCV) methods. Further, the proposed method provides improved spatial resolution and robustness in a multi-target environment.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.894
Threshold uncertainty score0.396

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.010
GPT teacher head0.261
Teacher spread0.251 · 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 designOther design
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

Citations2
Published2024
Admission routes1
Has abstractyes

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