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Cramer-Rao Lower Bound for Direction of Arrival Estimates Using Multi-Input Interferometric Receivers in Line-of-Sight Environment

2022· article· en· W4367147959 on OpenAlex
Bilel Mnasri, Halim Boutayeb, Larbi Talbi

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
TopicAntenna Design and Optimization
Canadian institutionsUniversité du Québec en Outaouais
Fundersnot available
KeywordsCramér–Rao boundUpper and lower boundsEstimatorAngle of arrivalDirection of arrivalInterferometrySignal-to-noise ratio (imaging)AlgorithmSIGNAL (programming language)Computer sciencePhysicsAcousticsEstimation theoryTelecommunicationsOpticsMathematicsAntenna (radio)Mathematical analysisStatistics

Abstract

fetched live from OpenAlex

This paper deals with the computation of the closed form expression of the Cramer-Rao Lower Bound (CRLB) relative to the estimates of the direction of arrival (DOA) of an electromagnetic plane wave, using distributed multi-input interferometers that use wave correlations to discriminate the phase of an unknown signal. The CRLB bound is found to be dependent on many design parameters, including the characteristic function of power detection diodes, the signal to noise ratio (SNR), as well as the number of receiving antennas. The new CRLB expression provides intuitive insight about the performance of any unbiased DOA estimator in the case of Line-of-Sight (LOS) propagation 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.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.628
Threshold uncertainty score0.378

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 head0.028
GPT teacher head0.239
Teacher spread0.211 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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