This paper focuses on an improved imaging algorithm for spotlight synthetic aperture radar (SAR) with continuous Pulse Repetition Interval (PRI) variation in extremely high-resolution. Conventional SAR systems are limited in that a wide swath cannot be achieved with a high azimuth resolution in the meantime. This limitation can be overcome by Pulse Repetition Frequency (PRF) variation in a SAR system. However, there are problems such as the ambiguities of point targets or extended targets caused by nonuniform sampling. A reconstructive method, Nonuniform Discrete Fourier Transform (NUDFT) has been presented in the current literature, but it is rather computationally expensive. In this paper, a modified sinc interpolation based on NUDFT is proposed, which is used to reconstruct the uniformly sampled echo in time domain. Since the interpolation kernel length is relatively short, it is more computationally efficient. Then, the two-step processing approach combined with the modified sinc interpolation is further presented, which has much better accuracy than that combined with the conventional sinc interpolation. Both the simulated data and the extracted GF-3 data experiment demonstrate the validity and accuracy of the proposed approach.

At present, synthetic aperture radar (SAR) automatic target recognition (ATR) has been deeply researched and widely used in military and civilian fields. SAR images are very sensitive to the azimuth aspect of the imaging geomety; the same target at different aspects differs greatly. Thus, the multi-aspect SAR image sequence contains more information for classification and recognition, which requires the reliable and robust multi-aspect target recognition method. Nowadays, SAR target recognition methods are mostly based on deep learning. However, the SAR dataset is usually expensive to obtain, especially for a certain target. It is difficult to obtain enough samples for deep learning model training. This paper proposes a multi-aspect SAR target recognition method based on a prototypical network. Furthermore, methods such as multi-task learning and multi-level feature fusion are also introduced to enhance the recognition accuracy under the case of a small number of training samples. The experiments by using the MSTAR dataset have proven that the recognition accuracy of our method can be close to the accruacy level by all samples and our method can be applied to other feather extraction models to deal with small sample learning problems.

Ship detection finds extensive applications in fisheries management, maritime rescue, and surveillance. However, detecting nearshore targets in SAR images is challenging due to land scattering interference and non-axisymmetric ship shapes. Existing SAR ship detection models struggle to adapt to oriented ship detection in complex nearshore environments. To address this, we propose an oriented-reppoints target detection scheme guided by scattering points in SAR images. Our method deeply integrates SAR image target scattering characteristics and designs an adaptive sample selection scheme guided by target scattering points. This incorporates scattering position features into the sample quality measurement scheme, providing the network with a higher-quality set of proposed reppoints. We also introduce a novel supervised guidance paradigm that uses target scattering points to guide the initialization of reppoints, mitigating the influence of land scattering interference on the initial reppoints quality. This achieves adaptive feature learning, enhancing the quality of the initial reppoints set and the performance of object detection. Our method has been extensively tested on the SSDD and HRSID datasets, where we achieved mAP scores of 89.8% and 80.8%, respectively. These scores represent significant improvements over the baseline methods, demonstrating the effectiveness and robustness of our approach. Additionally, our method exhibits strong anti-interference capabilities in nearshore detection and has achieved state-of-the-art performance.

Synthetic aperture radar (SAR) images are often affected by range ambiguity due to antenna sidelobe characteristics and pulse operating mechanism. The work of range ambiguity suppression focuses on both SAR system design and signal processing. On the one hand, ideas have been proposed to modify the transmitting and receiving waveforms to block the ambiguous energy, such as up and down chirp modulation, multiple elevation beams, and azimuth phase coding techniques. On the other hand, signal processing methods in echo and image domains have been used to reduce the range ambiguity energy, such as up and down chirp processing and sparse regularization methods. This article proposes a range ambiguity suppression method that combines sparse reconstruction and matched filter. There are four main steps: performing the sparse reconstruction to obtain the ambiguity area, estimating the ambiguity signal using the ambiguity area image and reconstruction matrix, separating the ambiguity signal from the whole signal to obtain the signal of the primary imaging area, and using the matched filter to the primary signal to obtain the main lobe area image. In this method, sparse reconstruction improves the accuracy of ambiguity signal estimation, and the matched filter ensures the efficiency of the imaging processing procedure. Simulation results show that the proposed method can effectively suppress range ambiguity. When the ambiguity energy is relatively strong, the focal underdetermined system solver algorithm can get better results than the classical orthogonal matching pursuit algorithm. Moreover, the weak targets and the details of the main lobe image are well preserved.

Multi-aspect Synthetic Aperture Radar (SAR) is a novel imaging mode, which supports high spatial resolution, 3D imaging and automatic target recognition. However, traditional SAR imaging algorithms will not only degrade SAR images, but also lose significant angular information. Adaptive sub-aperture imaging algorithms can solve these problems. Distinguishing between anisotropic targets and isotropic targets and extracting the persistence angle are two main steps in these algorithms, both of which will be improved in this paper. First, a better characteristic coefficient, namely amplitude statistical coefficient (ASC), is proposed to accurately distinguish between anisotropic targets and isotropic targets. Also, change-point detection is introduced to exactly extract the persistence angle. Besides, this letter attempts for the first time to quantitatively assess the characteristic coefficient indicating the anisotropy. For this purpose, two indicators are proposed: Anisotropy Discrimination Index (ADI) and Noise Resistance Range (NRR). The validity of this letter has been verified through the point target simulation data, the microwave anechoic chamber data, and the Gotcha public release data.