Towards Quantifying the Effect of Material Uncertainty on RCS Predictions of Composite Targets

被引：0

作者：

Kelley, Jon T. ^{[1
]}

Mackie-Mason, Brian ^{[1
]}

Chamulak, David A. ^{[1
]}

Martin, Mark ^{[1
]}

Crouch, Kendall ^{[1
]}

Courtney, Clifton C. ^{[1
]}

Yilmaz, Ali E. ^{[1
]}

机构：

[1] Lockheed Martin Aeronaut Co, Palmdale, CA 93599 USA

来源：

2024 INTERNATIONAL APPLIED COMPUTATIONAL ELECTROMAGNETICS SOCIETY SYMPOSIUM, ACES 2024 | 2024年

关键词：

Material Uncertainty; RCS;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

A Monte Carlo analysis is performed to study the sensitivity of the monostatic radar cross section (RCS) of thin-plate targets in the Austin RCS Benchmark Suite, which are shown to have high sensitivity to small variations in the material parameters. The RCS of the targets are found numerically for each material sample and the computational costs are reported. The case studies show that quantifying the impact of material uncertainty on the RCS (1) for a homogenous target model is not sufficient to quantify the impact of material uncertainty for a composite target model containing the same material, even if the other materials have no accompanying uncertainty, and (2) requires the use of fast methods even for electromagnetically small composite target models.

引用

页数：2

共 50 条

[1] Towards quantifying uncertainty in predictions of Amazon 'dieback'
Huntingford, Chris
Fisher, Rosie A.
Mercado, Lina
Booth, Ben B. B.
Sitch, Stephen
Harris, Phil P.
Cox, Peter M.
Jones, Chris D.
Betts, Richard A.
Malhi, Yadvinder
Harris, Glen R.
Collins, Mat
Moorcroft, Paul
[J]. PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES, 2008, 363 (1498) : 1857 - 1864
[2] Towards quantifying the uncertainty in in silico predictions using Bayesian learning
Allen, Timothy E.H.
Middleton, Alistair M.
Goodman, Jonathan M.
Russell, Paul J.
Kukic, Predrag
Gutsell, Steve
[J]. Computational Toxicology, 2022, 23
[3] Towards quantifying the uncertainty in in silico predictions using Bayesian learning
Allen, Timothy E. H.
Middleton, Alistair M.
Goodman, Jonathan M.
Russell, Paul J.
Kukic, Predrag
Gutsell, Steve
[J]. COMPUTATIONAL TOXICOLOGY, 2022, 23
[4] Quantifying Uncertainty in Predictions of Invasiveness
Peter Caley
W. M. Lonsdale
P. C. Pheloung
[J]. Biological Invasions, 2006, 8
[5] Quantifying uncertainty in predictions of invasiveness
Caley, P
Lonsdale, WM
Pheloung, PC
[J]. BIOLOGICAL INVASIONS, 2006, 8 (02) : 277 - 286
[6] Quantifying Uncertainty in Predictions of Hepatic Clearance
Rogers, James A.
Wilbur, Jayson
Cole, Susan
Bernhardt, Paul W.
Bupp, Jaye Lynn
Lennon, Morgan J.
Langholz, Nathan
Steiner, Christopher Paul
[J]. STATISTICS IN BIOPHARMACEUTICAL RESEARCH, 2011, 3 (04): : 515 - 525
[7] Towards Safe Deep Learning: Accurately Quantifying Biomarker Uncertainty in Neural Network Predictions
Eaton-Rosen, Zach
Bragman, Felix
Bisdas, Sotirios
Ourselin, Sebastien
Cardoso, M. Jorge
[J]. MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2018, PT I, 2018, 11070 : 691 - 699
[8] EFFECT OF ATMOSPHERIC PROPAGATION IN RCS PREDICTIONS
Alexopoulos, A.
[J]. PROGRESS IN ELECTROMAGNETICS RESEARCH-PIER, 2010, 101 : 277 - 290
[9] RCS Measurements and Predictions of different Targets for Radar Benchmark Purpose
Fernandez-Recio, R.
Jurado-Lucena, A.
Errasti-Alcala, B.
Poyatos-Martinez, D.
Escot-Bocanegra, D.
Montiel-Sanchez, I.
[J]. ICEAA: 2009 INTERNATIONAL CONFERENCE ON ELECTROMAGNETICS IN ADVANCED APPLICATIONS, VOLS 1 AND 2, 2009, : 443 - 446
[10] Towards Inverse Simulation: Effect of Material Parameters on Machining Predictions
Sela, A.
Ortiz-de-Zarate, G.
Soriano, D.
Cuesta, M.
Arrieta, I.
Arrazola, P. J.
[J]. PROCEEDINGS OF THE 22ND INTERNATIONAL ESAFORM CONFERENCE ON MATERIAL FORMING (ESAFORM 2019), 2019, 2113

← 1 2 3 4 5 →