For bistatic inverse synthetic aperture radar (Bi-ISAR) imaging of space targets, the impacts of scatterers’ migration through resolution cell (MTRC) and azimuth high-order phase (AHP) are becoming more severe, especially for a long coherent processing interval (CPI). In this paper, the movement of a scatterer is modeled as a quartic function under the restriction of the space target orbit, then an MTRC-AHP compensation algorithm is proposed. A fourth-order keystone transform is introduced to remove the MTRC firstly, then the residual AHP terms are compensated gradually via the azimuth compensation phase and fractional Fourier transform (FRFT). The impact of the orbit prediction error of the space target on the algorithm is analyzed theoretically. Numerical simulations illustrate the effectiveness of the proposed method and validation of the theoretical analysis.

In this paper, the spatial-variant property of ISAR imaging plane of space targets is exploited for phase unwrapping in bistatic interferometric inverse synthetic aperture radar (InISAR) imaging of space targets. We utilize the interferometric phases in different orbit arcs to obtain the correct fuzzy factor in a calculated set. The threshold for incorrect fuzzy factor excluding and the orbit arc selection strategy are derived for efficiency. Numerical simulations illustrate the effectiveness of the proposed method.

Image registration is essential for applications requiring the joint processing of inverse synthetic aperture radar (ISAR) images, such as interferometric ISAR, image enhancement, and image fusion. Traditional image registration methods, developed for optical images, often perform poorly with ISAR images due to their differing imaging mechanisms. This paper introduces a novel spatial feature-based ISAR image registration method. The method encodes spatial information by utilizing the distances and angles between dominant scatterers to construct translation and rotation-invariant feature descriptors. These feature descriptors are then used for scatterer matching, while the coordinate transformation of matched scatterers is employed to estimate image registration parameters. To mitigate the glint effects of scatterers, the random sample consensus (RANSAC) algorithm is applied for parameter estimation. By extracting global spatial information, the constructed feature curves exhibit greater stability and reliability. Additionally, using multiple dominant scatterers ensures adaptability to low signal-to-noise (SNR) ratio conditions. The effectiveness of the method is validated through both simulated and natural ISAR image sequences. Comparative performance results with traditional image registration methods, such as the SIFT, SURF and SIFT+SURF algorithms, are also included.

Multiband fusion imaging can effectively improve the range resolution of inverse synthetic aperture radar (ISAR) imaging. In this study, the block sparse Bayesian learning (BSBL) method is applied to multiband fusion imaging to achieve high‐resolution ISAR imaging of a block‐structured target. The BSBL method is suitable for the ISAR imaging of numerous and continuous scatterers because it considers the block structure characteristics of the signal. The validity of the proposed method is verified by the simulation and real‐data experimental results.

Phase‐coherence of signals in different sub‐bands is a prerequisite for multiband fusion radar imaging. Here, a mutual‐coherence compensation method based on valid dominant poles and phase autofocusing is proposed to compensate for the incoherent phases (ICPs) between sub‐bands in multiband fusion inverse synthetic aperture radar (ISAR) imaging. In the proposed method, the linear ICP compensation and fixed ICP compensation are decoupled rather than the ICPs being compensated pulse by pulse as in traditional methods. In this method, the valid dominant poles that correspond to strong scatterers in the estimated poles of each sub‐band are first extracted to estimate the linear ICP. Then, phase autofocusing technology based on the same rotation centre is applied to multiple pulses in each sub‐band in the ISAR coherently processing time to jointly compensate for all the fixed ICPs at once. Furthermore, a unitary estimation of signal parameters via the rotational invariance techniques (U‐ESPRIT) method, which can exploit considerable information from the measured data, is utilised to estimate the poles of the sub‐bands to improve the accuracy of the estimation of the linear ICP between sub‐bands. The effectiveness and robustness of the method were verified experimentally based on both numerical simulations and real data.

By exploiting the spatial-variant property of the inverse synthetic aperture radar (ISAR) imaging plane of a space target, interferometric ISAR (InISAR) can provide three-dimensional (3-D) images of space targets using fewer antennas than traditional InISAR imaging systems. This paper analyzes InISAR imaging of space targets with monostatic radar. First, a more realistic ISAR imaging model for space targets is established, which adopts the rotation of the earth and can provide a mathematical apprehension of the movement of space targets in InISAR imaging. Next, a sequential phase unwrapping method for InISAR imaging of space targets with monostatic radar is proposed. Then, the performance indicator of this one-antenna InISAR system is provided with analysis of the impact of the influencing factors in InISAR imaging under different scenarios. Finally, numerical simulations are performed to validate the theoretical analysis and effectiveness of the proposed method.