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Design of Series- fed Patch Array with Modified Binomial Coefficients for MIMO Radar Application

2021· article· en· W4213036580 on OpenAlexaff
Jogesh Chandra Dash, Debdeep Sarkar, Yahia M. M. Antar

Bibliographic record

Venue2021 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (APS/URSI) · 2021
Typearticle
Languageen
FieldEngineering
TopicRadar Systems and Signal Processing
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsSide lobeAzimuthMIMORadarTransmitterAcousticsComputer scienceElectronic engineeringAntenna (radio)TelecommunicationsPhysicsEngineeringOpticsChannel (broadcasting)

Abstract

fetched live from OpenAlex

In this paper, we propose a collocated antenna configuration having three transmitter and five receiver arrays for automotive MIMO Radar application. The individual antenna arrays consist of a seven-element series fed array with the feed provided at the edge. We introduce a modified-binomial coefficient-based excitation scheme to achieve improved side-lobe level (SLL) in series-fed patch arrays with edge feeding. The proposed MIMO configuration exhibits low SLL less than 16 dB and a wide field-of-view (FoV) of about 80° in the azimuth plane, along with satisfactory angular resolution (5.82°) and low cross-polar level (30 dB), rendering it extremely suitable for automotive medium-range radar (MRR) application.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.014
GPT teacher head0.235
Teacher spread0.221 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations21
Published2021
Admission routes1
Has abstractyes

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Same venue2021 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (APS/URSI)Same topicRadar Systems and Signal ProcessingFrench-language works237,207