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Resolution Enhancement of Radar Systems through Super Fusion of Non-Contiguous Frequency Bands

2024· article· en· W4404037076 on OpenAlexaff
Thomas Micallef, Xiaoqiang Gu, Ke Wu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced SAR Imaging Techniques
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsRadarFusionResolution (logic)Radar imagingRemote sensingImage resolutionComputer scienceGeologyArtificial intelligenceTelecommunications

Abstract

fetched live from OpenAlex

This work proposes and studies a super fusion procedure to stitch simultaneous, non-equal, and non-contiguous frequency bands together to increase the range and velocity resolution in FMCW radars. A comprehensive theoretical analysis is set to predict the resolution of such a super fusion procedure with several bands. A complete experimental verification has been conducted to verify the linear summation of the contribution of each band. Our procedure have successfully shown that three bands (B1= 300 MHz,B2= 200 MHz,B3= 100 MHz) with a measured resolution of 0.41m, 0.62m and 1.24m, respectively, can be fused together to obtain an equivalent 600 MHz band with a resolution of 0.32m. This super fusion procedure is believed to have great potential in reducing the actual RF congestion by splitting necessary radar bands into thinner sub-bands.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.010
GPT teacher head0.251
Teacher spread0.242 · 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 designBench or experimental
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

Citations0
Published2024
Admission routes1
Has abstractyes

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