Attitude determination of space target from an inverse synthetic aperture radar (ISAR) image sequence is an important but difficult task, because of the complex electromagnetic scattering characteristics of the target. To autonomously estimate the attitude of the space target from the ISAR image sequence, this letter introduces the key point feature extraction network (KPEN), and proposes a novel method for attitude determination via the key point feature extraction. The geometric relationship between the target attitude parameters and the key point feature is established through range-Doppler (RD) projection matrix. With the help of the key point extraction network, key point feature is extracted from ISAR image. Then the target attitude and its component size are autonomously recovered through key point feature by solving a nonlinear optimization. Extensive experiments show the effectiveness and superiority of the proposal.

With the rapid development of aerospace technology, especially the rise of the commercial aerospace industry, the number of space targets in orbit has increased rapidly. As aerospace activities are unprecedentedly active, developing space situation awareness technology has become an urgent need for space security. High-resolution imaging of space targets to obtain space target information is critical in space situational awareness. Compared to traditional 2D imaging, 3D imaging can provide complete information about a space target, which makes it a key technology for space situational awareness. However, due to the limitations of observation setups and imaging algorithms, all current 3D imaging methods tend to construct sparse 3D geometries and cannot infer 2D novel view images with geometry consistency. In this paper, we will focus on far-field imaging and propose ISAR-NeRF to address these problems, which encodes the 3D physical geometry of the target into an implicit neural network. According to the physical mechanisms of ISAR imaging, we can effectively determine sampling points from ISAR images and feed these sampling points into an implicit neural network to output densities and backscattering coefficients. We propose a novel differentiable radar rendering algorithm that exploits densities and backscattering coefficients to render multi-view consistent images and construct 3D geometries without any explicit association algorithms. The comparative experiments qualitatively and quantitatively show that ISAR-NeRF outperforms any other methods in both 3D reconstruction and novel view synthesis. In addition, the experiments of low SNR and noisy projection vectors demonstrate the robustness of our methods.

Abnormal dynamics awareness of space targets is critical for space surveillance. With the intrinsic range-Doppler projection mechanism, an inverse synthetic aperture radar (ISAR) image sequence naturally has crucial potential for abnormal dynamics interpretation on uncooperative targets. In this paper, we develop an automated deep neural network architecture for abnormal dynamic recognition of space targets from ISAR image sequences. With the accommodation of the stacked sparse autoencoder (SSAE) network joint sparsity constraint, the geometrical feature flow of an ISAR image sequence is learned to represent the target dynamics concisely. Then, the abnormal dynamic recognition is turned into a sequential classification task by exploiting the encoded feature flow through the long-short time memory (LSTM) network. Extensive experiment results confirm the superiority of the proposal in both precision and efficiency aspects.

Syntheticap erture radar (SAR) ship detection in harbors is challenging due to the similar backscattering of ship targets to surrounding background interference. Prevalent two-stage ship detectors usually use an anchor-based region proposal network (RPN) to search for the possible regions of interest on the whole image. However, most pre-defined anchor boxes are redundantly and randomly tiled on the image, manifested as low-quality object proposals. To address these issues, this paper proposes a novel detection method combined with two feature enhancement modules to improve ship detection capability. First, we propose a flexible anchor-free detector (AFD) to generate fewer but higher-quality proposals around the object centers in a keypoint prediction manner, which completely avoids the complicated computation in RPN, such as calculating overlapping related to anchor boxes. Second, we leverage the proposed spatial insertion attention (SIA) module to enhance the feature discrimination between ship targets and background interference. It accordingly encourages the detector to pay attention to the localization accuracy of ship targets. Third, a novel weighted cascade feature fusion (WCFF) module is proposed to adaptively aggregate multi-scale semantic features and thus help the detector boost the detection performance of multi-scale ships in complex scenes. Finally, combining the newly-designed AFD and SIA/WCFF modules, we present a new detector, named anchor-free two-stage ship detector (ATSD), for SAR ship detection under complex background interference. Extensive experiments on two public datasets, i.e., SSDD and HRSID, verify that our ATSD delivers state-of-the-art detection performance over conventional detectors.

Retrieval of attitude and geometry of satellite targets from inverse synthetic aperture radar (ISAR) images is an important but difficult task, because of the complex scattering phenomenon and motion-dependent projection mechanism. In this letter, we construct a novel component extraction network (CEN)-based oriented object detection to obtain the projection parameters of the target components from ISAR images, then combine this CEN with particle swarm optimization (PSO) algorithm to retrieve the 3-D attitude and geometry of the target components. This proposed method can be used to accurately estimate the attitude and geometry of the target. The simulation experiments show the effectiveness and superiority of this method.

With the continuous development of satellite constellations worldwide, dynamic estimation of on-orbit spacecraft plays a more and more important role in space situation awareness applications. Based on high-resolution inverse synthetic aperture radar (ISAR) imaging from the ground, some exploratory methods have been proposed to estimate target dynamic parameters, such as its attitude pointing and spin period. Due to the limited observation view from the ground, most of them rely on the long-term measurement, and the robust association of image features needs to be ensured. In this article, a novel approach based on spaceborne ISAR images is proposed to achieve the dynamic estimation of spin satellites. In order to build a synchronous similar-resolution ISAR imaging system, two adjacent satellites are picked up from low earth orbit constellations according to the radar tracking parameters. With obtained a pair of ISAR images, the explicit expression between target dynamic parameters and projection feature is deduced. Inspired by the existing works on shape extraction of the human body, a feature extraction network is built on the framework of ResNet for achieving the automatic processing in spaceborne equipments. In this way, target dynamic parameters can be solved in instantaneous attitude optimization and spin motion optimization in steps. Simulation experiments of a typical spin spacecraft, Tiangong-I, illustrate the feasibility of the proposed method. Besides, a comparison experiment with an existing ground-based work is also made to analyze its advantage in practical applications.