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Synthetic Aperture Radar three-dimensional (3D) imaging enables the acquisition of more comprehensive information, making it a recent hotspot in radar imaging. Traditional 3D imaging methods have evolved from 2D and interferometric imaging, combining elevation aperture extension with signal processing techniques. Limitations such as long acquisition or complex system from its imaging mechanism restrict its application. In recent years, rapid development of artificial intelligence has led to a swift advancement in radar, injecting new vitality into SAR 3D imaging. SAR microwave vision 3D imaging theory, which is built upon advanced technologies, has emerged as a new interdisciplinary field for radar imaging. This paper reviews SAR 3D imaging’s history and present situation, and introduces SAR microwave vision. We establish a theoretical framework covering representation models, computational models, processing paradigms and evaluation systems. Additionally, our research progress in this area is discussed, along with future prospects for SAR microwave vision 3D imaging.

Over time, Synthetic Aperture Radar (SAR) imaging techniques have been developed from twodimensional SAR, two-and-a-half-dimensional SAR (InSAR), and three-dimensional SAR to multi-dimensional SAR. This has led to great technological achievements. This paper briefly summarizes the development of SAR and its imaging technology, presents the concept of holographic SAR and clearly defines it for the first time, and highlights the differences and connection between the holographic SAR definition and existing concepts such as holographic radar, circular tomographic SAR, and multi-dimensional SAR. On this basis, under the framework of the existing multi-dimensional SAR techniques, the imaging system and signal model of holographic SAR are established, and preliminary imaging ideas are proposed, which provides a preliminary theoretical and technical framework for the development of holographic SAR technology.

Synthetic Aperture Radar three-Dimensional (SAR 3D) imaging technology can eliminate severe overlap in 2D images, and improve target recognition and 3D modeling capabilities, which have become an important trend in SAR development. After decades of development of SAR 3D imaging technology, many types of 3D imaging methods have been proposed. In this study, the history of SAR 3D imaging technology is systematically reviewed and the characteristics of existing SAR 3D imaging technology are analyzed. Given that the 3D information contained in SAR echo and images is not fully used by existing techniques, a new concept of SAR microwave vision 3D imaging has been proposed for the first time. This new concept is integrated with microwave scattering mechanism and image visual semantics to realize three-dimensional reconstruction, which form the theory and method of SAR microwave vision 3D imaging and can achieve high-efficiency and low-cost SAR 3D imaging. This study also analyzes the concept, goal and key scientific problems of SAR microwave vision 3D imaging and provides a preliminary solution, which will contribute in several ways to our understanding of SAR 3D imaging and provide the basis for further research.

Existing track-to-track association methods are mainly based on statistics and fuzzy mathematics.However, most methods based on statistics depend on thresholds, and parameters based on fuzzy mathematics are complex to set. In addition, most methods only consider the information of a single track point in comparison. To solve the existing problems, this paper presents a distance distribution histogram feature to extract the similarity features of a trajectory and measure them using the standardized Euclidean distances; this method effectively utilizes the characteristics of the whole trajectory and has a good anti-noise performance and accuracy. The motion features of ships and the location accuracy of different data sources were fully considered. After obtaining the histogram features of velocity difference and the source features of sensors, the authors combined them and trained association models using machine learning, which effectively avoids the problem of manually setting thresholds and complex parameter settings. Finally, a real ship data set was constructed. The experimental results show that compared with the traditional distance feature, the overall association accuracy was improved by 3.23%～11.65% using the distance distribution histogram feature, and by 0.068% using the combination feature, which verifies the effectiveness of the proposed method.

To solve the bottlenecks associated with traditional Synthetic Aperture Radar (SAR) systems related to the limitations of single working models and core indexes, a new system known as Multi-Input Multi-Output (MIMO) SAR was recently proposed. This novel radar system obtains greater freedom by the use of more equivalent transceivers. As such, it is not constrained by the limitations of traditional SAR systems and can achieve high-resolution wide-swath imaging and mean-time multi-mode operation. In this paper, we analyze the connotations and characteristics of MIMO-SAR in depth, and summarize the current research situation and future development trends. To lay a foundation for China’s future SAR technology, we have also developed the first MIMO-SAR prototype with the same time-frequency coverage. We summarize the key technologies and flight results and analyze the prospects for MIMO-SAR applications.

GF-3, the first full-polarimetric Synthetic Aperture Radar (SAR) satellite of China with a resolution up to 1 m, was successfully launched in August 2016 and, after 5 months of in-orbit calibration, it was officially delivered to the users in January 2017. In this paper, the geometric positioning error sources of the entire system are analyzed based on the real data acquisition, including atmospheric transmission, image processing, and geometric positioning. The positioning precision of the SAR system is validated by corner reflectors. The results show that the satellite positioning accuracy improved by 3 m.