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Impact of Pulse Width Parameter on the Bias of the CRLB Model in MIMO Radar

2023· article· en· W4388426720 on OpenAlex
Neda Rojhani, Marco Passafiume

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 Calgary
Fundersnot available
KeywordsCramér–Rao boundReliability (semiconductor)Upper and lower boundsRadarPulse-width modulationEstimation theoryComputer scienceMIMOAlgorithmBiasingElectronic engineeringMaximum likelihoodPulse (music)Control theory (sociology)MathematicsStatisticsPhysicsEngineeringTelecommunicationsDetectorPower (physics)Artificial intelligenceElectrical engineeringVoltage

Abstract

fetched live from OpenAlex

This paper deals with the effect of pulse width parameter on Cramér-Rao Lower Bounds (CRLB) model reliability for a widely distributed pulsed signal MIMO radar. When a model is incorrectly defined, it leads to an increase in CRLB estimation, yielding it useless as an instrument for defining system design parameters. The purpose of this study is to provide a theoretical description of such model biasing effect, particularly when dealing with a moving target, as well as to introduce practical conditions on the pulse width parameter to reduce the latter and overcome reliability reduction. The simulation results validate the theoretical description while also demonstrating the effectiveness of proposed conditions to reduce the bias effect.

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: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.147

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 head0.050
GPT teacher head0.261
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

Citations0
Published2023
Admission routes1
Has abstractyes

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