A Multi-Modal Fusion Algorithm for Space Target Recognition Based on Spatial Attention and Multi-Scale Temporal Network

When fusing inverse synthetic aperture radar (ISAR) images and high-resolution range profile (HRRP), the significant heterogeneity existing between the feature spaces of the two is not adequately considered, resulting in a low accuracy rate of space target recognition. A multi-modal fusion algorithm based on spatial attention and multi-scale temporal network is proposed in this paper. We carefully consider the data characteristics of HRRP and ISAR and design feature extraction networks, respectively. For HRRP, the local invariant features are extracted using dynamic convolution (DyConv), and the convolution depth is reduced. An improved multi-scale temporal convolution network is designed based on the temporal characteristics of HRRP to extract temporal features for target recognition. For ISAR images, an omnidirectional attention feature extraction module is designed to extract the deep semantic features of the images, and a noise reduction module with a spatial attention mechanism is designed before extracting the image features to reduce the background noise in the fused image. The superiority of the designed ISAR recognition network and HRRP recognition network for space target was verified through comparative and ablation experiments. The recognition rate for the target of the proposed algorithm is 98.41%.