Appropriate resource scheduling is the key to achieving the best performance of Low Earth-Orbit (LEO) Multi-Target Tracking (MTT) for the Space-based Radar Networks (SBRN) system. The high-velocity nature of satellites imposes high demands on the convergence rate of filtering and tracking accuracy while escalating the complexity of dynamic scheduling. To address these challenges, this paper utilizes Two-Line Elements (TLE) information to fully exploit the limited power-aperture resource of space-based Colocated Multiple-Input Multiple-Output (C-MIMO) radar and constructs a Walker constellation SBRN system. On this basis, a cognitive distributed cooperative tracking framework with fusion feedback mechanism is proposed. Each node can serve as the fusion center, achieving optimal fusion through the Fast Covariance Intersection (FCI) criterion while reducing the communication requirements. The global outcomes are fed back to all local nodes which can hasten the convergence rate of target state estimation. To maximize the global MTT performance, a closed-loop Joint Multi-Dimensional Resource Scheduling (JMDRS) strategy that considers multi-coverage conditions and visible windows is established. Moreover, the Posterior Cramér-Rao Lower Bound under Global Fusion Feedback (GF-PCRLB) is derived to provide a quantifiable metric for the overall performance. Finally, a fast suboptimal solution to the nonconvex model is proposed based on cross-iterative dimension reduction and variable decoupling. It introduces the tracking Efficiency-to-Cost Ratio (ECR) to jointly decide the beam pointing as well as the transmit power and fusion weight of each beam. Numerical results demonstrate that the proposed method significantly outperforms the existing approaches in enhancing the MTT performance of the SBRN system.

Low-angle tracking (LAT) with air surveillance radar is highly challenging due to the presence of multipath effect. Particularly, the multipath propagation is, in fact, both time-varying and unknown in a dynamic and complex environment, because of terrain variation, ground inclination and multiple reflection points. Further complexity is added by the fact that the low-angle environment is cluttered with incoherent interferences (e.g., birds). Therefore, a key problem lies in persistently estimating the direction of arrival of the low-angle target, in the presence of incoherent interferences and multipath coherent interferences. To address these issues, we have proposed a robust LAT method that explicitly accounts for detection imperfections in mixed interference environments. By integrating covariance matrix reconstruction for multisource separation with Bernoulli filtering for handling detection uncertainties, the proposed method enables robust LAT without prior knowledge of multipath propagation characteristics. Compared with the previous work, the proposed method can effectively mitigate the effects of mixed interferences, relaxing the assumption that the target is always correctly detected. Moreover, the Cramer-Rao lower bounds with arbitrary reflections are derived in a general, closed, and tractable form. Both numerical simulations and experimental results validate the effectiveness and robustness of the proposed method.

Locating low-altitude targets via air surveillance radar faces significant challenges due to the presence of multipath effects. Particularly, the multipath propagation is, in fact, both time-varying and unknown in a complex ground environment, arising from terrain variation, ground inclination, and multiple reflections. Conventional Direction-of-Arrival (DOA) estimation methods exhibit limited robustness, relying on prior knowledge of multipath propagation or terrain geometry. To address this issue, we propose an enhanced Reweighted Atomic Norm Minimization (RAM) method with three key contributions: (1) adaptive noise estimation that eliminates manual specification; (2) SNR-adaptive constraint tuning that automatically selects constraint tightness based on estimated signal quality; (3) ANM-based warm-start initialization to accelerate convergence. Numerical simulations demonstrate the effectiveness, efficiency, and robustness of the proposed method. Moreover, field experiments using an operational S-band array radar confirm robust performance without terrain priors, maintaining DOA estimation errors within +/- 0.1 Half-Power Beamwidth.

Inverse Synthetic Aperture Radar (ISAR) images of spacecraft are composed of discrete scattering points and exhibit characteristics such as weak texture, uneven intensity, and discontinuity, making it difficult for conventional 3D parameter estimation methods to accurately estimate the structural and attitude parameters of spacecraft. Considering that spacecraft components possess distinct geometric semantics, this paper proposes a primitive-based 3D parameter estimation method for spacecraft. The method first models the primitive structure of the spacecraft, then reconstructs the point cloud via the energy accumulation method, and finally estimates the 3D parameters by fusing the 2D contour features of ISAR images with the 3D geometric features of the reconstructed point cloud. Compared with traditional methods, the proposed method can characterize the geometric morphology of spacecraft more comprehensively and obtain the structural and attitude parameters required for space target surveillance. Experimental results verify the effectiveness of the proposed method.

3-D models of spacecraft can provide more detailed structural parameters than 2-D inverse synthetic aperture radar (ISAR) images. Therefore, reconstructing a spacecraft's 3-D structure from ISAR images is of great significance in space situational awareness. However, since spacecraft ISAR images are composed of discrete scattering points and exhibit characteristics such as weak texture, high dynamic range, and discontinuity, traditional factorization methods struggle to accurately match scattering points in ISAR images during 3-D reconstruction. Although the energy accumulation (EA) method proposed in recent years can skip the scattering point matching step, it is still affected by the differences in energy distribution between different components in ISAR images. To tackle the aforementioned issues, this article introduces a spacecraft 3-D reconstruction approach relying on component-level EA. First, an attention-based component segmentation network is designed to automatically segment the ISAR image sequence into cabin regions and solar panel regions. Second, a joint EA model for the cabin and solar panels is established, and this model is optimized using an adaptive particle swarm optimization algorithm. Finally, the symmetry of the component structure is utilized to complete the missing regions arising from self-occlusion in the reconstructed point cloud. This method does away with the requirement for extracting and matching scattering points, and effectively solves the problem of uneven component density in the reconstructed point cloud. Experimental results verify the effectiveness, robustness, and superiority of the proposed method, demonstrating that it can provide efficient and reliable 3-D model support for spacecraft on-orbit maintenance and fault diagnosis.

Slow-time coding techniques, including Code Division Multiple Access (CDMA), Doppler Division Multiple Access (DDMA), and joint CDMA–DDMA coding, are widely used in Multiple-Input Multiple-Output (MIMO) millimeter-wave radar systems to improve transmit-channel isolation, angular resolution, and field of view (FOV). However, traffic radar applications also require a high range resolution and long unambiguous detection range, which cannot be fully achieved by MIMO coding alone. This paper proposes a stepped-frequency Doppler-coded integrated joint division multiple access (SF-DC-JDMA) waveform that embeds stepped-frequency (SF) modulation into a jointly encoded CDMA–DDMA MIMO framework. In the proposed design, inter-group CDMA coding provides group-level transmit separation, intra-group DDMA modulation supports Doppler-domain Tx identification, and stepped-frequency synthesis improves range resolution. The resulting waveform combines multi-Tx orthogonality with synthesized wide-band ranging, enabling simultaneous channel separation, high-resolution range estimation, and long-range detection. The simulations and real-scene measurements demonstrate that, under the same range coverage, the proposed SF-DC-JDMA waveform achieves a significantly improved range resolution relative to the conventional FMCW waveform integrated by CDMA and DDMA modulation, and yields denser point-cloud representations of traffic targets.

State estimation of non-three-axis stabilized spacecraft is significant in space situational awareness tasks, such as on-orbit state analysis and collision warning. Traditional spacecraft state estimation methods based on Inverse Synthetic Aperture Radar (ISAR) images impose specific assumptions on the motion states of spacecraft and rely on long-term observation data to estimate state parameters. However, these methods exhibit strong dependency on the motion states and have certain limitations in practical applications. To address this issue, this paper proposes a state estimation method that does not depend on assumptions about the motion state. The method extends traditional ISAR observation to a double-station ISAR joint observation framework, leveraging dual-perspective measurements to decouple the relations between attitude parameters, motion parameters, and the projection morphology of spacecraft in ISAR images. A sequential estimation strategy for attitude and motion parameters is proposed: Firstly, by analyzing the double-station ISAR imaging geometry, the attitude parameters are estimated based on the invariance of the range projection vector. Then, an optimization function is constructed based on the cross-range projection morphology of the spacecraft to estimate the motion parameters. Moreover, the proposed deep multi-resolution keypoint extraction network substitutes traditional feature extraction methods. By autonomously extracting semantic features from ISAR images, it can effectively address the challenge of feature matching in double-station ISAR images. Experimental results validate the effectiveness and robustness of this method.

Tracking multiple targets hidden in the Doppler blind zone (DBZ) poses significant challenges for air surveillance radars, particularly when low-altitude targets employ a slow move-stop-move (S-MSM) strategy-alternating between moving and stationary states while remaining entirely within the DBZ-to evade radar tracking. The existing methods struggle because S-MSM targets cannot be reliably distinguished from fixed scatterers by motion alone. To address this issue, we proposed a filter built on the dual probability hypothesis density (DPHD) framework, an advanced Bayesian occupancy filter that efficiently separates dynamic and static environment components. The core insight lies in transforming the initial DPHD filter from tracking occupancy point masses to physical targets, simultaneously making it suitable for radar surveillance applications. First, we formulate an S-MSM model capturing DBZ-constrained state transitions between moving and stop modes. Second, we integrate amplitude information through adaptive survival and detection probability functions, enabling enhanced separation between dynamic targets and static clutter. Third, we introduce cluster-based target extraction to convert filtered occupancy grid cells into physical target estimates. Simulations with realistic surface clutter data show that the proposed method outperforms the benchmarks, demonstrating enhanced robustness and efficiency in time-varying, nonuniform clutter environments.

In order to obtain better inverse synthetic aperture radar (ISAR) image, a novel structure-enhanced spatial spectrum is proposed for estimating the incoherence parameters and fusing multiband. The proposed method takes full advantage of the original electromagnetic scattering data and its conjugated form by combining them with the novel covariance matrices. To analyse the superiority of the modified algorithm, the mathematical expression of equivalent signal to noise ratio (SNR) is derived, which can validate our proposed algorithm theoretically. In addition, compared with the conventional matrix pencil (MP) algorithm and the conventional root-multiple signal classification (Root-MUSIC) algorithm, the proposed algorithm has better parameter estimation performance and more accurate multiband fusion results at the same SNR situations. Validity and effectiveness of the proposed algorithm is demonstrated by simulation data and real radar data.

Optical observation of space targets frequently generates multi-exposure image sequences due to uneven illumination conditions, posing significant challenges to traditional Structure from Motion (SfM) methods. To address this issue, this paper proposes a novel SfM approach specifically designed for multi-exposure space target optical images. The method is built upon the COLMAP framework and innovatively integrates a self-supervised learning-based SuperPoint feature extractor with a SuperGlue matcher. By leveraging the self-supervised learning paradigm, the proposed method effectively overcomes the difficulties in feature point extraction and matching caused by exposure variations, thereby significantly improving the quality of sparse 3D reconstruction under multi-exposure conditions. To validate the method's effectiveness, experiments are conducted using multi-exposure space target image sequences. The results demonstrate that compared to the baseline method employing traditional SIFT features and sequential matching strategies, the proposed approach exhibits substantial advantages in reconstruction success rate, camera pose estimation accuracy, and sparse point cloud quality, confirming its robustness and effectiveness under non-uniform illumination conditions.

Space-based radars (SBRs) systems are able to provide an unobstructed field of view for space target detection and tracking. However, the large temperature dynamic range and poor heat dissipation performance of the SBR system cause severe thermal noise, leading to deficiency in distant or dim space target detection tasks. In essence, the challenges above can be categorized as typical low signal-to-noise ratio (SNR) problems, and the track before detect (TBD) processing scheme is applied to solve them in this article. Nevertheless, the typical TBD methods reckon without the following aspects and thus are not well compatible with space target surveillance tasks via the SBR system. First, the typical TBD methods discard the phase information of radar raw data in constructing the likelihood ratio. In addition, most existing work merely considers modeling the amplitude fluctuation as Swerling types, which is not accurate enough for space targets when compared with the log-normal distribution (LND) model. Moreover, orbital space targets follow the orbital dynamic principle while most existing TBD methods neglect this important information, which will cause space targets filtering estimation bias. To address the aforementioned problems, we propose a TBD method based on the complex-amplitude likelihood ratio (CLR) of the LND model and soft orbit-information constraint (OC). In this article, with the aim of acquiring a more accurate likelihood ratio, we first derive the closed mathematical form of the amplitude likelihood ratio (ALR) and the CLR of the LND model. Meanwhile, some approximations are proposed to alleviate the integral computation. Then, the proposed ALR and CLR of the LND model are utilized to be implemented into the TBD scheme. Finally, we design elegant soft OC strategies to modify the associated weights corresponding with birth particles in sequential Monte Carlo (SMC) implementation. Simulation results are provided to validate the effectiveness of the proposed soft OC-CLR-TBD method.

In this paper, a novel optimization framework based on 3D Gaussian splatting (3DGS) for high-fidelity 3D reconstruction of space targets under exposure bracketing conditions is studied. In the considered scenario, multi-view optical imagery captures space targets under complex and dynamic illumination, where severe inter-frame brightness variations degrade reconstruction quality by introducing photometric inconsistencies and blurring fine geometric details. Unlike existing methods, we explicitly address these challenges by integrating exposure-aware adaptive refinement and edge-preserving regularization into the 3DGS pipeline. Specifically, we propose an exposure bracketing-oriented bounding box (OBB) regional densification strategy to dynamically identify and refine under-reconstructed regions. In addition, we introduce a Sobel edge regularization mechanism to guide the learning of sharp geometric features and improve texture fidelity. To validate the framework, experiments are conducted on both a custom OBR-ST dataset and the public SHIRT dataset, demonstrating that our method significantly outperforms state-of-the-art techniques in geometric accuracy and visual quality under exposure-bracketing scenarios. The results highlight the effectiveness of our approach in enabling robust in-orbit perception for space applications.

Optical image sequences of spacecraft acquired by space-based monocular cameras are typically imaged through exposure bracketing. The spacecraft feature deformable alignment network for multi-exposure image fusion (SFDA-MEF) aims to synthesize a High Dynamic Range (HDR) spacecraft image from a set of Low Dynamic Range (LDR) images with varying exposures. The HDR image contains details of the observed target in LDR images captured within a specific luminance range. The relative attitude of the spacecraft in the camera coordinate system undergoes continuous changes during the orbital rendezvous, which leads to a large proportion of moving pixels between adjacent frames. Concurrently, subsequent tasks of the In-Orbit Servicing (IOS) system, such as attitude estimation, are highly sensitive to variations in multi-view geometric relationships, which means that the fusion result should preserve the shape of the spacecraft with minimal distortion. However, traditional methods and unsupervised deep-learning methods always exhibit inherent limitations in dealing with complex overlapping regions. In addition, supervised methods are not suitable when ground truth data are scarce. Therefore, we propose an unsupervised learning framework for the multi-exposure fusion of optical spacecraft image sequences. We introduce a deformable convolution in the feature deformable alignment module and construct an alignment loss function to preserve its shape with minimal distortion. We also design a feature point extraction loss function to render our output more conducive to subsequent IOS tasks. Finally, we present a multi-exposure spacecraft image dataset. Subjective and objective experimental results validate the effectiveness of SFDA-MEF, especially in retaining the shape of the spacecraft.

Attitude estimation of noncooperative spacecraft based on a monocular camera is a crucial technique in on-orbit servicing missions. Most of the existing methods rely on the known 3-D model of the target or require a large number of observation images with ground-truth labels, which do not apply to unseen spacecraft lacking such prior knowledge. In this article, we present a two-stage framework for semantic feature extraction of spacecraft typical components and on-orbit attitude estimation of unseen targets to solve the above problem, which inverts the 3-D attitude information from the 2-D axes of spacecraft typical components in the sequential observation images. First, a spacecraft semantic feature network (SSF-Net) is designed, which can learn the common semantic features of typical components in different spacecraft, thereby achieving good generalization for unseen targets and extracting their axes features. Then, we introduce homographic adaptation, the geometric constraint of semantic features, and dynamic constraints in sequential images to optimize false positives or missed detections of the extracted features under extreme observation perspectives. Finally, the axis reconstruction algorithm based on the random sample consensus (RANSAC) is proposed to estimate the attitude of unseen on-orbit spacecraft. Simulation results confirm that the proposed method can effectively extract semantic features, with average pixel and angular errors of 6.93 pixels and 1.86 degrees, respectively, and estimate the attitude of unseen spacecraft with typical component structures accurately, achieving the average estimation error of 3.25 degrees. Experiments also exhibit significant advantages compared to classical methods and excellent robustness under worse observing conditions.

Feature point detection in inverse synthetic aperture radar (ISAR) images of space targets is the foundation for tasks such as analyzing space target motion intent and predicting on-orbit status. Traditional feature point detection methods perform poorly when confronted with the low texture and uneven brightness characteristics of ISAR images. Due to the nonlinear mapping capabilities, neural networks can effectively learn features from ISAR images of space targets, providing new ideas for feature point detection. However, the scarcity of labeled ISAR image data for space targets presents a challenge for research. To address the issue, this paper introduces a self-supervised feature point detection method (SFPD), which can accurately detect the positions of feature points in ISAR images of space targets without true feature point positions during the training process. Firstly, this paper simulates an ISAR primitive dataset and uses it to train the proposed basic feature point detection model. Subsequently, the basic feature point detection model and affine transformation are utilized to label pseudo-ground truth for ISAR images of space targets. Eventually, the labeled ISAR image dataset is used to train SFPD. Therefore, SFPD can be trained without requiring ground truth for the ISAR image dataset. The experiments demonstrate that SFPD has better performance in feature point detection and feature point matching than usual algorithms.

To investigate the feasibility and performance of seismic wave sensors for detecting low-altitude targets, the study designed real-world experiments with R44 helicopter. During the experiments, seismic waves and ground truth data were collected by sensors and RTK (Real-Time Kinematic) module, respectively. Time-frequency analysis results indicated that the performance of sensors, which was primarily affected by the flight altitude and range of targets. Moreover, further analysis confirmed that the seismic waves were chiefly sourced from the main rotor of the helicopter. The experimental results validate the feasibility of detecting helicopters with seismic wave sensors as theoretically expected.

Discrimination of approaching multiple space targets is a challenging task for space situational awareness (SSA). Compared with ground-based radars, space-based radars cover better coverage and they have no restriction on the curvature of the earth. In this paper, we utilize space-based radar to track objects in space. Note that radar cross section (RCS) characteristics and Doppler information are typical potential target individual features to improve the tracking performance for SSA tasks. However, one of the limitations of utilizing the RCS information is that the fluctuation of targets dynamic RCS is modeled by the typical $\chi^2$ distribution or Weibull distribution. In practice, fluctuating RCS of typical space targets such as mission-focused space targets, fit poorly with the above distribution models under some altitude angles. To tackle this problem, we proposed a mixed log-normal (MLN) distribution model to compute the intensities of time-varying dynamic RCS likelihood. Then the dynamic RCS likelihood and Doppler information are incorporated into the update recursion of the standard probability hypothesis density (PHD) filter. Furthermore, considering that multiple space-closed targets are misjudged as one under space targets tracking scenarios, an adaptive threshold is devised to merge the Gaussian mixture (GM) components weights to ease this phenomenon simultaneously. The number estimation preservation proof is given and the GM implementation of the proposed filter is also provided in this paper. Simulation results validate the effectiveness and robustness of the proposed RCS & Doppler information aided & GM weights improved probability hypothesis density (RDGI-PHD) filter.

A space-based bistatic radar system composed of two space-based radars as the transmitter and the receiver respectively has a wider surveillance region and a better early warning capability for high-speed targets, and it can detect focused space targets more flexibly than the monostatic radar system or the ground-based radar system. However, the target echo signal is more difficult to process due to the high-speed motion of both space-based radars and space targets. To be specific, it will encounter the problems of Range Cell Migration (RCM) and Doppler Frequency Migration (DFM), which degrade the long-time coherent integration performance for target detection and localization inevitably. To solve this problem, a novel target detection method based on an improved Gram Schmidt (GS)-orthogonalization Orthogonal Matching Pursuit (OMP) algorithm is proposed in this paper. First, the echo model for bistatic space-based radar is constructed and the conditions for RCM and DFM are analyzed. Then, the proposed GS-orthogonalization OMP method is applied to estimate the equivalent motion parameters of space targets. Thereafter, the RCM and DFM are corrected by the compensation function correlated with the estimated motion parameters. Finally, coherent integration can be achieved by performing the Fast Fourier Transform (FFT) operation along the slow time direction on compensated echo signal. Numerical simulations and real raw data results validate that the proposed GS-orthogonalization OMP algorithm achieves better motion parameter estimation performance and higher detection probability for space targets detection.