DocumentCode :
75942
Title :
Efficient iterative solution of electromagnetic scattering using adaptive cross approximation enhanced characteristic basis function method
Author :
Xinlei Chen ; Changqing Gu ; Zhuo Li ; Zhenyi Niu
Author_Institution :
Key Lab. of Radar Imaging & Microwave Photonics, Nanjing Univ. of Aeronaut. & Astronaut., Nanjing, China
Volume :
9
Issue :
3
fYear :
2015
fDate :
2 19 2015
Firstpage :
217
Lastpage :
223
Abstract :
A new hybrid adaptive cross approximation-characteristic basis function method (ACA-CBFM) is proposed to efficiently solve the electromagnetic scattering problems. In the conventional ACA-CBFM, the ACA is only applied to speed up the construction of the reduced matrix that is directly solved and stored. However, with the increase of the size of the targets under analysis, the reduced matrix will become so large that it is difficult to directly solve and store. In this study, the reduced matrix is further compressed by the adaptive cross approximation-singular value decomposition (ACA-SVD) and solved by an iterative method, which leads to reduced storage and accelerated matrix vector product. Furthermore, the ACA-SVD is adapted to efficiently generate the characteristic basis functions (CBFs), which reduces both the time of generating initial CBFs and the SVD time of initial CBFs. Numerical results about the electromagnetic scattering from perfect electric conducting targets are given to demonstrate the merits of the proposed methods.
Keywords :
approximation theory; electromagnetic wave scattering; iterative methods; singular value decomposition; vectors; ACA-CBFM; ACA-SVD; adaptive cross approximation-singular value decomposition; electromagnetic scattering problems; hybrid adaptive cross approximation-characteristic basis function method; iterative method; perfect electric conducting targets; reduced accelerated matrix vector product; reduced matrix construction; reduced storage matrix vector product;
fLanguage :
English
Journal_Title :
Microwaves, Antennas & Propagation, IET
Publisher :
iet
ISSN :
1751-8725
Type :
jour
DOI :
10.1049/iet-map.2013.0624
Filename :
7047313
Link To Document :
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