Abstract
With insufficient training samples, the available inverse synthetic aperture radar (ISAR) target recognition methods based on deep convolutional neural networks (DCNNs) have degraded performance or even become invalid. To deal with this issue, a few-shot ISAR target recognition method based on Gaussian prototypical network (GPN) is proposed. In GPN, ISAR images are firstly mapped into embedding vectors by the embedding network. Then, Gaussian prototypes are constructed according to the weighted embedding vectors. Finally, target categories are predicted according to the Mahalanobis distance between test samples and prototypes. Recognition results of measured data consisting of three categories of aircraft show that the proposed method can obtain high recognition accuracy when there are only a few training samples.
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