Journal
PROGRESS IN ELECTROMAGNETICS RESEARCH M
Volume 76, Issue -, Pages 91-99Publisher
ELECTROMAGNETICS ACAD
DOI: 10.2528/PIERM18082907
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Funding
- Research Grants Council of Hong Kong [GRF 17209918, GRF 17207114, GRF 17210815]
- AOARD [FA2386-17-1-0010]
- NSFC [61271158]
- HKU Seed Fund [104005008]
- Hong Kong UGC [AoE/P-04/08]
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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.
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