Inverse synthetic aperture radar (ISAR) imaging for maneuvering targets suffers from a Doppler frequency time-varying problem, leading to the ISAR images blurred in the azimuth direction. Given that the traditional imaging methods have poor imaging performance or low efficiency, and the existing deep learning imaging methods cannot effectively reconstruct the deblurred ISAR images retaining rich details and textures, an unblurring ISAR imaging method based on an advanced Transformer structure for maneuvering targets is proposed. We first present a pseudo-measured data generation method based on the DeepLabv3+ network and Diamond-Square algorithm to acquire an ISAR dataset for training with good generalization to measured data. Next, with the locally-enhanced window Transformer block adopted to enhance the ability to capture local context as well as global dependencies, we construct a novel Uformer-based GAN (UFGAN) to restore the deblurred ISAR images with rich details and textures from blurred imaging results. The simulation and measured experiments show that the proposed method can achieve fast and high-quality imaging for maneuvering targets under the condition of a low signal-to-noise ratio (SNR) and sparse aperture.

When inverse synthetic aperture radar (ISAR) images a high-speed maneuvering target, the imaging results of traditional methods are blurred and defocused in the range and azimuth directions because of the high speed and acceleration of the target. The existing imaging methods of maneuvering targets have poor imaging effects or low efficiency, and can’t eliminate the migration through range cells (MTRC) of far points on the target. Given that the existing methods can’t meet the imaging requirements of high-speed maneuvering targets, a new method by combing the UNET3+ convolution neural network (CNN) to machting Fourier transform (MFT) imaging process is proposed in this paper. Overcoming the limitations of the existing imaging methods, the proposed method can realize fast and accurate imaging for high-speed maneuvering targets. Experiments show that the novel MFT-UNET3+ method can achieve a good focus even under the condition of a low signal-to-noise ratio and down-sampling.

Air-ground collaborative systems (AGCS), in which unmanned aerial vehicles survey from above while ground vehicles navigate the terrain below, can achieve capabilities far beyond those of any single platform when they share perception and coordinate action. AGCS are undergoing a three-stage paradigm shift: from rule- and reinforcement learning-driven programmed execution, through vision–language navigation that introduces a language interface, to MLLM-driven autonomous perception, reasoning, and heterogeneous collaboration. Taking this paradigm shift as its organizing thread, this review critically examines how large language models (LLMs) and multimodal large language models (MLLMs) are reshaping the intelligence paradigm of air-ground heterogeneous collaborative systems between 2023 and 2026. We argue that the core of this transformation is not the substitution of one technical tool for another, but a change in how AGCS understand tasks, coordinate heterogeneous agents, and reason in unknown environments. They are moving from reliance on pre-specified structures toward semantic understanding and autonomous decision-making. Organized around a three-stage framework, the review covers collaborative perception, vision–language navigation (VLN), vision–language–action (VLA) models, multi-agent collaboration, and air-ground heterogeneous MLLM coordination. We analyze the capability boundaries of MLLMs in air-ground settings and outline the key challenges and roadmap from proof-of-concept to generalizable frameworks.