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Record W2097513256 · doi:10.1109/ccece.1996.548245

Adaptive beamforming with near-instantaneous convergence for matched filter processing

2002· article· en· W2097513256 on OpenAlexaff
A.C. Dhanatltwari, Stergios Stergiopoulos, William J. Phillips, William Robertson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicDirection-of-Arrival Estimation Techniques
Canadian institutionsTechnical University of Nova Scotia
Fundersnot available
KeywordsAdaptive filterBeamformingAdaptive beamformerComputer scienceSonar signal processingArray processingMarine mammals and sonarFilter (signal processing)Covariance matrixConvergence (economics)AlgorithmSignal processingMatched filterSpace-time adaptive processingBroadbandCoherence (philosophical gambling strategy)SonarMathematicsDigital signal processingArtificial intelligenceTelecommunicationsComputer vision

Abstract

fetched live from OpenAlex

We have investigated the implementation of relatively new broadband adaptive processing schemes in line array sonar systems with passive and active capabilities. We provide details of the investigation of adaptive beamforming schemes with and instantaneous convergence for matched filter processing. These schemes include a so-called memoryless generalised sidelobe canceller (GSC) beamformer and a coherent broadband adaptive algorithm that is based on a space-time statistic called the steered covariance matrix. Application results from synthetic and real experimental data show that the adaptive processing schemes provide continuous beam time series at the input of a matched filter. In addition, the adaptive beam time series have sufficient temporal coherence and correlate with the reference FM signals of active sonars, which indicates that the algorithms have achieved near-instantaneous convergence.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.029
GPT teacher head0.242
Teacher spread0.213 · 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 designSimulation or modeling
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

Citations7
Published2002
Admission routes1
Has abstractyes

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