Semisupervised Space Target Recognition Algorithm Based on Integrated Network of Imaging and Recognition in Radar Signal Domain

The application of deep learning to inverse synthetic aperture radar (ISAR) target recognition helps to improve accuracy in space target monitoring. However, the orbit transfer and maneuver of space targets are likely to cause range migration in radar echoes. Information deficiency of scattering points and the limitations of data acquisition methods pose enormous challenges to space target imaging and recognition. To address these issues, this article proposes a semisupervised space target recognition algorithm based on an integrated network of imaging and recognition in the radar signal domain. By directly processing radar signals, the algorithm can achieve high-precision space target imaging and recognition under the general situation and the conditions of range migrations. Based on the inherent characteristics of radar complex echoes, the algorithm utilizes unlabeled echoes to generate pseudolabels to achieve better generalization capabilities. Both real and complex convolutions are exploited to generate high-resolution features. Besides, feature optimization modules are designed to effectively integrate high-resolution texture features and contour features to magnify the difference between the target and background. The ablation experiments and contrast experiments indicate that, under the conditions of migration and missing components, the algorithm can obtain high-resolution features for imaging and recognition by only using a small amount of labeled data. Thus, the algorithm achieves high accuracy and robustness for migrated space target recognition and has superiority over other algorithms.