Complex-Valued Multiscale Vision Transformer on Space Target Recognition by ISAR Image Sequence
Haoxuan Yuan, Hongbo Li, Yun Zhang, Chenxi Wei, Ruoyu Gao
Abstract
In recent years, researches on the recognition for Inverse Synthetic Aperture Radar (ISAR) images continue to deepen, while most methods only use the amplitude information of the ISAR image data. Besides, high-order terms in the complex-valued (CV) received signals for maneuvering space targets will cause defocusing on the ISAR images, which affects the accuracy of the recognition. For a steadily rotating maneuvering target, its high-order phase information between frames is relevant, and this information can be used to facilitate recognition. To this end, this letter proposes an end-to-end recognition framework in the CV domain based on the transformer model. It uses multi-scale feature extraction strategy and CV attention mechanism to get the local and global hybrid feature. Besides, A spatio-temporal transformer (STT) block is proposed to obtain the spatio-temporal correlation between image frames to assist recognition. Finally, a residual CNN block is introduced to promote diversity in the captured representations. In the experimental part, the recognition results of the proposed method on the real and simulated dataset are better than those of other methods. Compared with the classic sequence recognition method CVLSTM, the recognition accuracy and kappa coefficient of the proposed method are increased by approximately 5.6% and 5.4% respectively.