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Record W2142390157 · doi:10.1109/vetecf.2000.886639

Coherent interference suppression with an adaptive array using spatial affine projection algorithm

2002· article· en· W2142390157 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Adaptive Filtering Techniques
Canadian institutionsCarleton University
Fundersnot available
KeywordsAlgorithmDecorrelationAdaptive filterComputer scienceSmoothingComputational complexity theoryConvergence (economics)Interference (communication)MathematicsChannel (broadcasting)

Abstract

fetched live from OpenAlex

This paper proposes a new algorithm, namely the spatial affine projection (SAP) algorithm, for adaptive array beamforming in the presence of multiple coherent interferers. The SAP algorithm applies the affine projection (AP) algorithm to adaptive beamformers in the space domain. Combining the decorrelation property of the AP algorithm with that of the spatial smoothing (SS) algorithm, the SAP algorithm achieves coherent interference suppression and fast convergence simultaneously. Computer simulation shows that the SAP algorithm incorporating a subtractive preprocessor (SP) outperforms the existing algorithms by placing deeper nulls and converging faster. The convergence of the SAP algorithm is comparable to that of the SS scheme using the RLS algorithm while the computational complexity of the SAP algorithm is close to that of the SS scheme using the normalized LMS algorithm.

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 score0.721
Threshold uncertainty score0.668

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 communication0.0000.000
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 head0.040
GPT teacher head0.249
Teacher spread0.210 · 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

Quick stats

Citations5
Published2002
Admission routes1
Has abstractyes

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