Attitude estimation for space targets based on inverse synthetic aperture radar (ISAR) is essential for various on-orbit operations, including spacecraft status analysis and fall forecast. However, traditional attitude estimation methods based on ISAR images suffer from poor feature extraction performance. Furthermore, the present methods can only deal with stabilized spacecraft. In situations where a non-cooperative spacecraft is in a slow-spinning state, the ISAR cross-range scaling is inaccurate, resulting in incorrect attitude estimation. In this study, we propose a cross-range scaling and attitude estimation method that can be applied to slow-spinning spacecraft. The method transforms attitude estimation to a parameter optimization task, where the optimization model aims to recover the cross-range scaling factor and attitude. Firstly, the slow-spinning state is parameterized and the optimization model of cross-range scaling and attitude estimation is established. Secondly, semantic features are extracted from ISAR images by the proposed multi-scale semantic feature extraction network. The optimization model is then solved through semantic features using the proposed robust estimation algorithm. Ultimately, the correct cross-range scaling factor and attitude of corresponding ISAR image are acquired. This paper makes three contributions: 1. A specialized mathematics model to perform cross-range scaling and attitude estimation of slow-spinning model is proposed. 2. By substituting traditional feature points with semantic features, the reliability of ISAR feature extraction is improved. 3. The proposed robust estimation algorithm enhances the accuracy of parameter estimation. Experiments demonstrate that this method has higher cross-range scaling and attitude estimation accuracy for slow-spinning spacecraft than usual methods.

The 2-D inverse synthetic aperture radar (ISAR) images of space targets vary with the viewing angles and are not suitable for automatic target recognition (ATR), thus requiring 3-D reconstruction techniques. Conventional 3-D space target reconstruction techniques either use monostatic radar with incoherent processing for target retrieval or exploit multistatic radar but coherent processing for target reconstruction. In this article, we combined aspects of monostatic radar and coherent processing to investigate the coherent reconstruction of 3-D space target reflectivity with monostatic radar. Space objects periodically revisit the same ground area. Multipass interferometric synthetic aperture radar (multipass InSAR) uses repeat-pass synthetic aperture radar (SAR) acquisitions on the same ground area with a spaceborne SAR system for 3-D ground surface reconstruction. Motivated by multipass InSAR, we used multipass ISAR acquisitions on the same space target with monostatic ground radar for 3-D space target reconstruction. We proposed a multipass interferometric ISAR (multipass InISAR) algorithm that exploits inverse synthetic aperture technology for range-azimuth focusing and interferometry for the third dimension reconstruction. The elevation estimation performance of the algorithm is analyzed. Simulation results on the Tiangong-2 space lab, Jason-3 geodetic satellite, and ENVISAT remote sensing satellite validate the effectiveness of the proposed algorithm.

Cross-range scaling of sequential inverse synthetic aperture radar (ISAR) images is a vital problem for the three-dimensional (3D) reconstruction of spatial targets. Specifically, since the angular velocity of spatial targets cannot be accurately estimated due to their non-cooperative nature, the obtained cross-range coordinates of the scattering points often have non-negligible errors. The errors may result in a serious distortion of the reconstructed geometry of the targets, which makes consequent target recognition and classification unreliable. To tackle this problem, via geometrical analysis, the relationship between the cross-range projection vector and the range projection vector is revealed for the first time, based on which a novel algorithm is proposed to iteratively scale the cross-range coordinates with no need for estimating the angular velocity of the spatial targets. Experimental simulations show that the proposed algorithm not only provides a higher accuracy of 3D reconstruction, but also has a lower computational complexity compared with conventional algorithm.

Instantaneous attitude estimation for spacecraft through optical/ISAR fusion observation is a hot topic in space situational awareness. However, due to the imaging characteristics and domain gap between the two data types, it is difficult to autonomously estimate instantaneous attitude based on data fusion. In this paper, an automatic optical/ISAR multi-feature fusion instantaneous attitude estimation framework is proposed. We deploy semantic feature extraction networks to extract and associate two kinds of robust features in optical and ISAR images, and an attitude estimation algorithm considering cross-range scaling based on multiple features combination is proposed. Meanwhile, the inherent structure constraints of spacecraft are introduced. Simulation experiments demonstrate that the proposed framework achieves an average attitude estimation accuracy of 1.87°, while also exhibiting excellent robustness.

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.

The performance of classic radar geometry based on the line-of-sight (LOS) signal transmitted from radar to the target in the free space is affected by multipath echoes in urban areas, where non-line-of-sight (NLOS) signals reflected by obstacles are received by the radar. Based on prior information of the urban situation, this article proposes a novel two-stage localization algorithm with multipath exploitation in a dense clutter environment. In the offline stage, multipath propagation parameters of uniformly distributed samples in the radar field of view are predicted by the ray-tracing technique. In the online stage, a rough location of the target is estimated by the maximum similarity between measurements and the predicted parameters of reference samples at different locations. The similarity is described by the likelihood between measurements and the predicted multipath parameters with respect to all possible associated hypotheses. A gating threshold is derived to exclude less likely hypotheses and reduce the computational burden. The accurate target location is acquired by a non-linear least squares (NLS) optimization of the associated multipath components. Simulation results in various noise conditions show that the proposed method provides robust and accurate target localization results under dense clutter conditions, and the offline pre-calculation of ray-tracing ensures the real-time performance of the proposed localization algorithm. The root mean square error (RMSE) of simulation results shows the advantage of the proposed method over the existing method. The presented results suggest that the proposed method can be applied to NLOS target localization applications in complex environments.