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Record W2171861278 · doi:10.1109/icassp.2011.5947062

Transmit beamspace design for direction finding in colocated MIMO radar with arbitrary receive array

2011· article· en· W2171861278 on OpenAlexaff
Arash Khabbazibasmenj, Aboulnasr Hassanien, Sergiy A. Vorobyov

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRadar Systems and Signal Processing
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMIMOComputer scienceRadarTransmitter power outputConvex optimizationRelaxation (psychology)AlgorithmBeamformingRegular polygonMathematical optimizationElectronic engineeringMathematicsTelecommunicationsGeometryEngineeringTransmitter

Abstract

fetched live from OpenAlex

The transmit beamspace design problem for colocated multiple-input multiple-output (MIMO) radar is considered. We show that the MIMO radar transmit beampattern can be designed so that it is as close as possible to the desired one, the power is uniformly distributed across the transmit antennas, and most significantly, the rotational invariance property at the receive array with arbitrary geometry is satisfied. The latter enables a straightforward application of search-free direction of arrival estimation techniques such as ESPRIT in the unconventional case with the receive array of arbitrary geometry. The transmit beamspace design problem is cast as an optimization problem which is non-convex in general, but can be solved efficiently using the semi-definite programming relaxation technique.

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)0.000
metaresearch head score (Gemma)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.033
GPT teacher head0.209
Teacher spread0.176 · 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

Citations17
Published2011
Admission routes1
Has abstractyes

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