Harnessing the Power of SVD: An SVA Module for Enhanced Signal Classification

Deep learning methods have achieved outstanding performance in various signal tasks. However, due to degraded signals in real electromagnetic environment, it is crucial to seek methods that can improve the representation of signal features. In this paper, a Singular Value decomposition-based Attention, SVA is proposed to explore structure of signal data for adaptively enhancing intrinsic feature. Using a deep neural network as a base model, SVA performs feature semantic subspace learning through a decomposition layer and combines it with an attention layer to achieve adaptive enhancement of signal features. Moreover, we consider the gradient explosion problem brought by SVA and optimize SVA to improve the stability of training. Extensive experimental results demonstrate that applying SVA to a generalized classification model can significantly improve its ability in representations, making its recognition performance competitive with, or even better than, the state-of-the-art task-specific models.