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Record W2990755211 · doi:10.3390/electronics8121447

MIMO Radar Using a Vector Network Analyzer

2019· article· en· W2990755211 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

VenueElectronics · 2019
Typearticle
Languageen
FieldEngineering
TopicRadar Systems and Signal Processing
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsMIMOElectronic engineeringRadarComputer scienceBandwidth (computing)Network analyzer (electrical)Spectrum analyzerSignal processingRadar engineering detailsContinuous-wave radarEngineeringRadar imagingTelecommunicationsBeamforming

Abstract

fetched live from OpenAlex

In this paper, a multiple-input multiple-output (MIMO) radar system was developed using a Keysight’s N5244A 4-port PNA-X network analyzer and Simulink. The system can transmit and receive TDM stepped-frequency continuous wave signals with a total sweep bandwidth of 450 MHz. The system also provides a reliable, self-contained phase-coherent RF front-end across four RF channels, which is a critical requirement for MIMO Radar signal processing algorithms. A Simulink model was built to organize the collected S-parameters into a virtual array and to perform IFFT processing so that range and angle information from targets could be extracted. The experimental results show the ability of the MIMO radar to distinguish between multiple closely spaced targets with a 33 cm range resolution and a 19o angle resolution.

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 score0.279
Threshold uncertainty score0.517

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 head0.007
GPT teacher head0.202
Teacher spread0.195 · 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