Complex-Valued Multiscale Vision Transformer on Space Target Recognition by ISAR Image Sequence

In recent years, research on the recognition for inverse synthetic aperture radar (ISAR) images continues 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 a multiscale feature extraction strategy and a CV attention mechanism to get the local and global hybrid feature. Besides, a spatiotemporal transformer (STT) block is proposed to obtain the spatiotemporal correlation between image frames to assist recognition. Finally, a residual convolutional neural network (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 datasets are better than those of other methods. Compared with the classic sequence recognition method CV long short-term memory (CVLSTM), the recognition accuracy and kappa coefficient of the proposed method are increased by approximately 5.6% and 5.4%, respectively.