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77 Ghz Series-Fed $4 \times 7$ Antenna Array with Enhanced Sidelobe Suppression for Automotive Radars

2025· article· W4417132081 on OpenAlexaff
Moncef Kadi, M. M. Hasan Mahfuz, Ahmed A. Kishk

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsConcordia UniversityUniversité du Québec à Rimouski
Fundersnot available
KeywordsAzimuthAntenna (radio)HFSSAntenna arrayReturn lossMicrostrip antennaReflective array antennaElevation (ballistics)Aperture (computer memory)Planar array

Abstract

fetched live from OpenAlex

The design and analysis of a series-fed microstrip patch antenna array for an automotive radar at 77 GHz are presented. The antenna allows object detection within a 10 to 250 meter range. It meets the required stringent beamwidths:$\boldsymbol{\pm} \mathbf{1 5}^{\circ}$in the azimuth plane and$\pm 5^{\circ}$in the elevation plane, which can precisely cover the field of view while minimizing undesired vertical spread. The series-fed array offers high directivity, compact form factor, and a simplified feeding network attributes considered critical for space- and cost-constrained automotive integration of radar. Low sidelobes are achieved by enforcing Taylor's aperture distribution for the azimuthal and elevation radiation patterns. Accordingly, several comprehensive HFSS simulations are made to verify the performance. The antenna resonates at 77 GHz, achieving a return loss of 22.5 dB and a peak gain of around 19.9 dB. The final configuration is a planar array of$4 \times 7$elements that has achieved the desired beamwidths.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.002

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 head0.006
GPT teacher head0.224
Teacher spread0.217 · 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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