Solution of electrically large scattering problems using the characteristic basis function method

Li, C; Mittra, R

Solution of electrically large scattering problems using the characteristic basis function method

Li, C Mittra, R

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Publication Type:: Conference Proceeding
Citation:: ISAP 2016 - International Symposium on Antennas and Propagation, 2017, pp. 562 - 563
Issue Date:: 2017-01-17

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Field	Value	Language
dc.contributor.author	Li, C	en_US
dc.contributor.author	Mittra, R https://orcid.org/0000-0002-0960-2708	en_US
dc.date.issued	2017-01-17	en_US
dc.identifier.citation	ISAP 2016 - International Symposium on Antennas and Propagation, 2017, pp. 562 - 563	en_US
dc.identifier.isbn	9784885523137	en_US
dc.identifier.uri	http://hdl.handle.net/10453/126560
dc.description.abstract	© 2016 IEICE. In this paper, parallel multilevel characteristic basis function method (MLCBFM) has been hybridized with the randomized pseudo-skeleton approximation method (RSPA) and randomized singular value decomposition (rSVD) method, for the analysis of the scattering from electrically large rough surfaces. MLCBFM defines the Characteristic Basis Functions (CBFs) on a larger domain and thereby achieves a higher compression rate. The reduced matrix has a much smaller size compared to that of the original system, which enables us to use a direct solver, rather than an iterative one. The RSPA algorithm accelerates the generation of the reduced matrix while the rSVD expedites the generation of CBFs. The hybrid method is found to be both accurate and efficient, and we use it in this work to investigate the problem of bistatic scattering from Gaussian rough surfaces.	en_US
dc.relation.ispartof	ISAP 2016 - International Symposium on Antennas and Propagation	en_US
dc.title	Solution of electrically large scattering problems using the characteristic basis function method	en_US
dc.type	Conference Proceeding
utslib.for	1005 Communications Technologies	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2016 IEICE. In this paper, parallel multilevel characteristic basis function method (MLCBFM) has been hybridized with the randomized pseudo-skeleton approximation method (RSPA) and randomized singular value decomposition (rSVD) method, for the analysis of the scattering from electrically large rough surfaces. MLCBFM defines the Characteristic Basis Functions (CBFs) on a larger domain and thereby achieves a higher compression rate. The reduced matrix has a much smaller size compared to that of the original system, which enables us to use a direct solver, rather than an iterative one. The RSPA algorithm accelerates the generation of the reduced matrix while the rSVD expedites the generation of CBFs. The hybrid method is found to be both accurate and efficient, and we use it in this work to investigate the problem of bistatic scattering from Gaussian rough surfaces.

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http://hdl.handle.net/10453/126560