Abstract
This paper proposes a novel source reconstruction method (SRM) based on the convolutional neural network algorithm. The conventional SRM method usually requires the scattered field data oversampled compared to that of target object grids. To achieve higher accuracy, the conventional SRM numerical system is highly singular. To overcome these difficulties, we model the equivalent source reconstruction process using the machine learning. The equivalent sources of the target are constructed by a convolutional neural networks (ConvNets). It allows us to employ less scattered field samples or radar cross section (RCS) data. And the ill-conditioned numerical system is effectively avoided. Numerical examples are provided to demonstrate the validity and accuracy of the proposed approach. Comparison with the traditional NN is also benchmarked. We further expand the proposed method into the direction of arrival (DOA) estimation to demonstrate the generality of the proposed procedure.
Recommended Citation
H. M. Yao et al., "Applying Convolutional Neural Networks For The Source Reconstruction," Progress In Electromagnetics Research M, vol. 76, pp. 91 - 99, Progress In Electromagnetics Research M, Jan 2018.
The definitive version is available at https://doi.org/10.2528/PIERM18082907
Department(s)
Electrical and Computer Engineering
International Standard Serial Number (ISSN)
1937-8726
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Progress In Electromagnetics Research M, All rights reserved.
Publication Date
01 Jan 2018
Comments
Glaucoma Research Foundation, Grant 17207114