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- W2990971594 abstract "In this report, the element failure detection of array antenna is performed with a minimum number of measurement points while maintaining sufficient accuracy by learning the relationship between excitation coefficients of array antenna and the electric “far-field” pattern by a shallow neural network. When training the neural network, the massive number of training data are generally required. For increasing the training data, we use each element-fed far-field distribution multiplied by a number of random excitation coefficients. In the case of linear dipole array antennas of 17 elements, the estimation error of excitation coefficients of array antenna less than 0.4% are achieved by our trained neural network with a minimum number of far-field measurements with 50 dB SNR." @default.
- W2990971594 created "2019-12-05" @default.
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- W2990971594 date "2019-10-01" @default.
- W2990971594 modified "2023-09-24" @default.
- W2990971594 title "Element Failure Detection of Array Antenna using Near-field Measurement with Shallow Neural Network" @default.
- W2990971594 doi "https://doi.org/10.23919/amtap.2019.8906457" @default.
- W2990971594 hasPublicationYear "2019" @default.
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