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Record W2170587520 · doi:10.1109/cemtd.2007.4373510

FDTD Modelling of a Realistic Room for Through-the-Wall Radar Applications

2007· article· en· W2170587520 on OpenAlexaff
W. Chamma

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrowave Imaging and Scattering Analysis
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsFinite-difference time-domain methodRadarMultistatic radarBistatic radarComputer scienceRadar imagingRemote sensingAcousticsTime domainGeologyOpticsComputer visionPhysicsTelecommunications

Abstract

fetched live from OpenAlex

This work describes the use of the finite-difference time-domain (FDTD) method to investigate the capabilities and limitations in the use of a UWB radar system, to detect a human model target inside a realistic room. A 0.8 ns pulse at a center frequency of f <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> = 1.1 GHz is used. The room is modelled with all its components (water & heating pipes, windows, electrical outlets, brick walls, barred windows, etc.). The radar setup is also modelled to simulate a realistic system. The performance of the UWB radar is examined by generating 2-dimensional (2D) images of the room interior with the human body model included. This is done by considering monostatic and multistatic radar scenarios where responses are recorded and processed using the time-domain back-projection method. Images from the FDTD modelling detected the human body model at the correct location inside the room. As expected, the multistatic radar setup provided a cleaner image than that of the monostatic radar setup due to greater diversity in aspect angle.

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 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: Methods · Consensus signal: none
Teacher disagreement score0.924
Threshold uncertainty score0.256

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 head0.027
GPT teacher head0.247
Teacher spread0.220 · 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 teacher head, 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

Citations9
Published2007
Admission routes1
Has abstractyes

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