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Record W2103757191 · doi:10.1109/icassp.2011.5947063

Subspace-based direction finding using transmit energy focusing in MIMO radar with colocated antennas

2011· article· en· W2103757191 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
TopicRadar Systems and Signal Processing
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMIMORadarEnergy (signal processing)Computer scienceElectronic engineeringSubspace topologyTelecommunicationsAlgorithmEngineeringMathematicsBeamformingStatisticsArtificial intelligence

Abstract

fetched live from OpenAlex

In this paper, we consider the problem of direction finding in multiple-input multiple-output (MIMO) radar based on focusing the transmitted pulse energy within certain spatial sector(s). We propose a method for designing the transmit weight matrix based on maximizing the energy transmitted within the desired spatial sector and minimizing the energy disseminated in the out-of-sector area. The proposed transmit energy focusing results in the signal-to-noise ratio increase at the receive array which in turn leads to lower Cramer-Rao bound and improved direction of arrival estimation performance. Simulation results show the substantial improvements offered by the proposed transmit energy focusing based MIMO radar as compared to the traditional MIMO radar and the MIMO radar with receive beamspace post-processing.

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: Empirical
Teacher disagreement score0.571
Threshold uncertainty score0.673

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.028
GPT teacher head0.196
Teacher spread0.168 · 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

Citations10
Published2011
Admission routes1
Has abstractyes

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