# ISAR Imaging Analysis of Complex Aerial Targets Based on Deep Learning
## 1 Introduction  
Radar target recognition technology is one of the most significant research directions in the development of modern radar systems [1]. Over the past few decades, numerous pioneering accomplishments have been achieved in both theoretical research and engineering applications. The key areas of radar target recognition, such as the theoretical analysis of the electromagnetic scattering mechanism of radar targets, the description and feature extraction of target scattering characteristics, and pattern recognition technology, have been thoroughly investigated [2, 3, 4]. With the advancement of radar technology, serving as the foundation of current synthetic aperture radar (SAR) and inverse synthetic aperture radar (ISAR) imaging, the combination of high radial range resolution resulting from wide bands and high lateral range resolution brought about by high Doppler resolution generates a two-dimensional scattering image of the target, providing more abundant and detailed information about a target. It enables location, orientation, shape, and structure of the target to be determined, which can be utilized for classification and recognition [5, 6, 7, 8]. Consequently, target scattering feature extraction based on the two-dimensional image of radar targets has emerged as an effective approach for obtaining target features in complex electromagnetic environments. Analyzing the scattering characteristics of three-dimensional electrically large and complex targets is a highly challenging research task [9]. In recent years, many advanced electromagnetic field calculation methods, such as the physical optics method, geometric optics method, physical diffraction theory, geometric diffraction theory, and equivalentedge electromagnetic current method, have been widely utilized in academic circles [10]. The theoretical modeling of complex objects typically employs numerous approaches, such as three-dimensional reconstruction, component decomposition, and the surface element method. However, for three-dimensional electrically large and complex objects, the performance of algorithms is often limited when relying solely on a single electromagnetic field calculation method. Consequently, when addressing different types of targets, some researchers flexibly combine various methods for calculations, taking into account the differences in the physical characteristics of their scattering centers. In view of the substantial differences between the basic theory and practical engineering modeling, some experts tend to employ the component decomposition method to simplify complex objects into standard and regular geometric structures (such as corner reflectors, plates, etc.) for more precise approximate analysis [11]. In recent years, the rapid advancement of deep learning has led to the employment of specially designed neural networks for various radar imaging applications. The deep convolutional neural network (CNN) [12] is utilized to suppress the sidelobes of ISAR images. Neural networks, particularly deep neural networks, typically require substantial amounts of data. To address the challenge of insufficient training data, simulated data are employed, with target images obtained through traditional range-Doppler (RD) processing being input into the model. The ISAR images generated from the ideal point model serve as a reference. A Complex CNN (Complex CNN, CCNN) was proposed in [12] due to the inherent complexity of radar data. Deep neural networks have also been applied to SAR imaging in the presence of motion errors [13], where SAR imaging is framed as a sparse recovery problem [13] and resolved by unfolding the neural network. The deep network is subsequently retrained using simulated data. Experimental results demonstrate that the learned model is effectively transferable from simulated data to the application of actual radar data. This study builds upon our previous research [14]. In this paper, we introduce several significant enhancements and expansions.