Space Targets with Micro-Motion Classification Using Complex-Valued GAN and Kinematically Sifted Methods

Space target classification based on micro-motion characteristics has become a subject of great interest in the field of radar, particularly when using deep learning techniques. However, in practical applications, the ability of deep learning is hampered by the available radar datasets. As a result, obtaining a sufficient amount of the training dataset is a daunting challenge. To address this issue, this paper presents a novel framework for space target classification, consisting of three distinct modules: dataset generation, the kinematically sifted module, and classification. Initially, the micro-motion model of cone-shaped space targets is constructed to analyze target characteristics. Subsequently, the dataset generation module employs a complex-valued generative adversarial network (CV-GAN) to generate a large number of time-range maps. These maps serve as the foundation for training the subsequent modules. Next, the kinematically sifted module is introduced to eliminate images that do not align with the micro-motion characteristics of space targets. By filtering out incompatible images, the module ensures that only relevant and accurate dataset is utilized for further analysis. Finally, the classification model is constructed using complex-valued parallel blocks (CV-PB) to extract valuable information from the target. Experimental results validate the effectiveness of the proposed framework in space micro-motion target classification. The main contribution of the framework is to generate a sufficient amount of high-quality training data that conforms to motion characteristics, and to achieve accurate classification of space targets based on their micro-motion signatures. This breakthrough has significant implications for various applications in space target classification.