Terahertz (THz) inverse synthetic aperture radar (ISAR) imaging of space target has the advantage of high resolution and all-day capability for spatial situational awareness (SSA). However, compared with microwave ISAR imaging, micro vibrations of radar platform and target can cause images defocusing and quality degradation in THz band. Commonly, the existing algorithms considered vibration as simple harmonic motion in rigid body, and estimated the vibration parameters for phase error compensation. Nevertheless, these methods are dependent on the prominent points and cannot focus on the complex non-rigid vibration of the space target. Aiming at these problems, an ISAR imaging framework that can be leveraged for complex non-rigid vibration compensation is proposed. First, a compressed sensing (CS) model is established to compensate for the vibration phase error in ISAR imaging. Then, the alternating direction method of multipliers (ADMM) is employed to optimize the CS model. Subsequently, considering that strong scattering points of space targets will cover up the weak scattering information in ISAR images, a sidelobe suppression constraint is appended. Thus, the weak scattering information is retained while removing the sidelobe and noise. It is worth mentioning that the ISAR imaging algorithm is derived to a two dimension (2-D) matrix form to reduce memory usage and to improve computational efficiency. Finally, simulated and measured data are utilized to verify the superiority of the proposed algorithm, and proves the potential of ISAR imaging for practical space targets.

As demand for precise detection of space targets and orbital maintenance increases, it is crucial to obtain high-resolution images of space targets. The terahertz (THz) radar with carrier frequency of 0.1-10 THz has the characteristics of larger bandwidth and shorter wavelength compared with microwave radar. Accordingly, under the same inverse synthetic aperture radar (ISAR) imaging conditions, it has advantages in both range resolution and azimuth resolution theoretically. However, for large-size space targets, the phenomenon of high-order range cell migration (RCM) and azimuth phase error are prone to occur during small-rotation-angle (3-5°) ISAR imaging, which results in severe imagery defocusing. In this article, a high-resolution ISAR imaging method based on defocused point spread function (PSF) is proposed. By means of third-order Taylor expansion performing on the Range-Doppler (RD) imaging model, the impact of high-order terms on imaging is analyzed. Through revealing the relationship between high-order terms and inclination of defocused PSF measured with Hough Transform (HT), the rotation velocity and range center deviation (RCD) are estimated accurately and independently in image domain. After parameters estimation, the first-order RCM is corrected by Keystone Transform (KT). Utilizing the estimated rotation velocity, the second-order RCM is eliminated by Fast Gaussian Gridding Nonuniform Fast Fourier Transform (FGG NUFFT). Then the spatial-variant phase error is compensated based on previous estimation. Lastly, azimuth resolution is improved by FGG NUFFT due to non-uniformly sampled data. Numerous simulations and real measured experimental results demonstrate the effectiveness and outperformance of proposed algorithm for large-size space target ISAR imaging in the THz band.

The high-resolution inverse synthetic aperture radar (ISAR) imaging of space target holds tremendous significance for recognition and on-orbit maintenance. The terahertz (THz) radar possesses natural superiority in terms of large bandwidth and short wavelength, enabling it to achieve higher resolution both in range and azimuth direction. However, the range cell migration (RCM) induced by rotation is severe in the terahertz band, even in small rotation angle. In order to achieve high-accuracy RCM correction, an imaging algorithm based on Keystone Transform (KT) and Fast Gaussian Gridding Nonuniform Fast Fourier Transform (FGG NUFFT) is presented in this article. Nevertheless, to correct the second-order RCM and compensate the spatial-variant (SV) phase error, the rotation parameters of uncooperative target need to be estimated first. Moreover, by analyzing the ISAR images of space targets, it is found that the scattering intensity of certain components on the satellite body, such as the cavity structure of the antenna, is stronger than that of the solar panels. Considering scattering intensity of these components, the traditional estimation method under global image quality criteria may fail. To prevent getting trapped in a local optimum or finding an incorrect solution due to the influence of strong scattering points, a parameter estimation framework based on prior knowledge added Sparrow Search Algorithm (PKSSA) under local maximum contrast criterion (LMCC) is brought forward. The proposed novel algorithm can not only fast estimate rotation parameters without being affected by strong scattering points, but also obtain fine imaging results. Numerous electromagnetic simulation and real measured terahertz experiment results prove the correctness and effectiveness of proposed algorithm.

Vibration induces a micro-Doppler effect, which contains crucial motion information and features of the micro-motion target. However, the traditional radar can only acquire micro-motion information in the radial direction. The multi-channel radar is capable of acquiring actual micro-motion information in a three-dimensional space, including amplitude, frequency, and three-dimensional direction of vibration. Moreover, the high-accuracy parameter estimation can be achieved in the terahertz band. In this paper, a method for three-dimensional micro-motion feature exaction and parameter estimation is proposed, including a phase-derived-range algorithm, interference, and auto-correlation. Meanwhile, to prove feasibility of the method, the results of simulations and experiment based on a 0.22 THz multi-channel radar system are presented after theoretical analyses.