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.

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.

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.

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.

Interrupted-sampling repeater jamming (ISRJ) is a kind of intra-pulse coherent deception jamming that can generate false target peaks in the range profile and interfere with the detection and tracking of real targets. In this paper, an anti-ISRJ method based on the intra-pulse orthogonal waveform is proposed, which can recognize common interference signals by comparing sub-signal matched filtering results. For some special scenes where real targets cannot be directly differentiated from false targets, a new recognition method based on the energy discontinuity of the interference signal in the time domain is proposed in this paper. The method proposed in this paper can recognize real and false targets in all ISRJ modes without any prior information, such as jammer parameters, with a small amount of calculation, which is suitable for actual radar systems. Simulation experiments using different interference parameters show that although this method has a 3 dB loss of pulse compression gain, it can completely suppress different kinds of ISRJ interference when the SNR before pulse compression is higher than −20 dB, with 100% target detection probability.

Microwave over‐the‐horizon (OTH) propagation through troposcatter is a valuable candidate for remote targets detection. Array antenna is promising to handle the contradiction between real‐time wide spatial coverage requirement and high antenna gain to capture weak scattering signal. The detection tactics and performance of array receiving systems are significantly affected by the fading correlation between array antennas, but there is no analytical study which considers the fading correlation in troposcatter passive sensing environments for uncooperative targets. In this study, an analytical method based on a scattering transfer function is proposed to derive the channel fading correlation, and the result is a function of transmitter deviation and spatial, temporal, frequency, angular separations of receiving array antennas in uncooperative troposcatter circumstances. In addition, the OTH troposcatter array receiving signal model is derived by exploiting the fading correlation to form the covariance structure of the received array signal, adding up with the signal transmission loss. Existing experimental results validate the derived fading correlations, and the simulation results validate the OTH passive detection performance based on the provided signal model.