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2018-11-27
Applying Convolutional Neural Networks for the Source Reconstruction
By
Progress In Electromagnetics Research M, Vol. 76, 91-99, 2018
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 employless 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.
Citation
He Ming Yao Wei E. I. Sha Li Jun Jiang , "Applying Convolutional Neural Networks for the Source Reconstruction," Progress In Electromagnetics Research M, Vol. 76, 91-99, 2018.
doi:10.2528/PIERM18082907
http://www.jpier.org/PIERM/pier.php?paper=18082907
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