High-Resolution ISAR Imaging Method for Maneuvering Targets Based on a Hybrid Transformer

Range instantaneous Doppler (RID) methods show superior advantages in imaging performance over range-Doppler (RD) algorithms for maneuvering targets. However, traditional RID methods are susceptible to noise and sparse aperture (SA), which makes the resolution of imaging results low and the quality degraded. To improve the imaging performance, we propose an RID high-resolution (HR) imaging framework for maneuvering targets, in which a hybrid transformer-based HR network (HTHRNet) is introduced into the RID method. HTHRNet is trained to map low-resolution (LR) time–frequency distribution (TFD) images to HR TFD images under different conditions. Our deep neural network fuses the convolutional neural network (CNN) and transformer to make full use of their advantages. In addition, we modify the network structure to make HTHRNet training easier and more efficient. The experimental results of TFD data, simulated data, microwave chamber data, and measured data show that the proposed imaging method has superior performance, which further indicates that the complex deep neural network can be well applied to ISAR HR imaging.