Range instantaneous Doppler (RID) methods show superior advantages in imaging performance over range-Doppler (RD) algorithm 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 a 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.

Deep learning has been used in inverse synthetic aperture radar (ISAR) imaging to improve resolution performance, but there still exist some problems: the loss of weak scattering points, over-smoothed imaging results, and the universality and generalization. To address these problems, an ISAR resolution enhancement method of exploiting a generative adversarial network (GAN) is proposed in this paper. We adopt a relativistic average discriminator (RaD) to enhance the ability of the network to describe target details. The proposed loss function is composed of feature loss, adversarial loss, and absolute loss. The feature loss is used to get the main characteristics of the target. The adversarial loss ensures that the proposed GAN recovers more target details. The absolute loss is adopted to make the imaging results not over-smoothed. Experiments based on simulated and measured data under different conditions demonstrate that the proposed method has good imaging performance. In addition, the universality and generalization of the proposed GAN are also well verified.

The micro-Doppler (m-D) effect induced by micro-motion parts of the target degrades the quality of inverse synthetic aperture radar (ISAR) imaging results. To solve this issue, this article proposes a data-driven deep network framework to remove the m-D effect in ISAR imaging, so as to obtain better imaging results. First, a new training data generation method is proposed, which is suitable for both point targets and block targets. Through this method, a plenty of m-D ISAR images and label images without m-D effect can be obtained, which can be used as training data. Then we improve the UNet as our training network, namely IUNet. After that, training data are used to train the IUNet. After training, the network learns the mapping relationship between training data, and the network model is saved. Finally, the well-trained network can remove the m-D effects in test ISAR images. The experiments of simulated data and measured data show that the proposed method is superior in terms of eliminating m-D effect than other methods and has good performance.

The deception jamming method based on Digital Radio Frequency Memory (DRFM) for Inverse Synthetic Aperture Radar (ISAR) has been a widely studied topic in recent decades. Typically, jamming signals generated using two-dimensional or three-dimensional false target models can create realistic false targets on the ISAR image. However, existing deception jamming methods cannot eliminate or revise the original echo, which can be retained by multiple anti-jamming methods once the radar judges out receiving the jamming signal. Additionally, these methods require large storage space for the models. Otherwise, the false targets cannot be generated realistically. To address these issues, this paper proposes a jamming signal generation algorithm based on two-dimensional template multiplication modulation and template time delay. The frequency shift and time delay relationship between the signals intercepted by the jammer and the real target echo is analyzed and derived in detail. With the use of these detailed derivations, it is possible to add and remove scatters by precisely locating the false scatter on the real ISAR image. The real target’s shape naturally changes as a result of the addition and removal of scatters. Furthermore, this method can adaptively change the resolution of the false target’s ISAR image with the radar pulse width and the accumulated pulse number. Meanwhile, the false target size on the ISAR image can be adjusted adaptively by altering the false template resolution. These features of the proposed method offer increased flexibility and efficiency for deception jamming. By accurately determining the position of false scatter on the ISAR image, this method offers improved performance compared with the existing techniques. Simulation results demonstrate the effectiveness of the proposed deception jamming method.

The micro‐Doppler (m‐D) effect caused by the rotational parts of the targets influences the quality of inverse synthetic aperture radar (ISAR) imaging. In this letter, a novel deep network‐assisted method is proposed to reduce the m‐D effect in ISAR imaging. The training data, including ISAR images with m‐D effect and ISAR images without m‐D effect, help the network establish non‐linear mapping relationships. The simulated and measured data results show the effectiveness of the proposed method.

In recent decades, the deception jamming approach based on digital radio frequency memory (DRFM) for inverse synthetic aperture radar (ISAR) has been a well-researched topic. Various types of jamming signals based on interrupted-sampling repeater jamming (ISRJ) can induce one-dimensional multiple high-resolution range profile (HRRP) false targets or two-dimensional realistic multiple ISAR false targets for deception. However, these existing methods generate false targets that are identical to the real target. The ISRJ false target also generates a main false target whose energy is much higher than other sub-false targets. Thus, it is easy to discover that the radar has suffered jamming. In order to generate better, more confusing jamming signals, this paper proposes a jamming method based on sub-Nyquist sampling jamming to induce realistic, multiple false targets on ISAR images. It improves a template multiplication modulated time-delay method to eliminate and add scatterers on the selected false target to change its ISAR shape. The frequency-shift parameters of the template jamming signal are analyzed and derived in detail for eliminating and adding scatterers at the specified location. Thus, multiple false targets with different ISAR shapes and similar energy are generated, which can create better deception effects. Meanwhile, this method can adjust the number of false targets and the location of changed-shape false targets. Furthermore, the resolution of the false target can adaptively change with the radar pulse width and the accumulated pulse number. The simulation results show that the proposed deception jamming strategy works.