Terahertz (THz) radar imaging can achieve high range resolution for transmitting of wideband signals. High cross-range resolution can be achieved simultaneously with small rotation angles for the tiny wavelength. However, the radar echo should be sampled according to the Nyquist theory and the imaging should ensure that the unambiguous range is large than the size of targets, which limits its application from both data collection and data storage perspectives. In this paper, a bistatic THz radar azimuth-elevation imaging scheme based on compressed sensing is proposed. By exploiting the sparsity of the space distribution in the imaging plane, it is sufficient to reconstruct images of targets with a small number of random measurements within the radar aperture. This means that the limitation for the high resolution THz imaging of large targets can be overcome. The available high resolution bistatic images of targets, which show the target as it is observed, can be very useful for scattering understanding and target recognition at THz frequencies. Simulation results by the electromagnetic calculation data are presented to demonstrate the validity of the proposed method.

To deal with the perfect electric conductor rough targets’ scattering problem, which is the basic premise of the radar applications at terahertz frequencies, a hierarchical semideterministic modeling and full-wave-algorithm-based computation method was proposed. The scattering fields with phase information and synthetic aperture images of electrically large cubes with different surface characteristics were obtained. The influence of the surface roughness can be easily observed. According to the imaging results, we can predict that other than the scattering calculation challenges caused by the surface roughness, it also brings great benefits that more detailed information about the targets will emerge on the images.

To enhance air defense efficiency in a Distributed Multiple Input Multiple Output (D-MIMO) radar sensor network (RSN) for detecting low-altitude targets, this paper introduces a cooperative game theoretic power allocation (CGTPA) strategy within power budget constraints. The CGTPA mechanism involves precise power management to effectively mitigate variations and fading in the received signal caused by multipath effects. Thereby, it can capitalize on the abundant scattering characteristics within a multipath environment to achieve enhanced target detectability. The paper introduces the concept of multipath distance difference (MDD) by incorporating errors from space-based external information, and subsequently derives the Neyman-Pearson based detection probability and signal-to-interference-plus-noise ratio (SINR). Further, SINR is utilized as the metric for RSN target detection performance, and an optimization model is established based on the Max-Min criterion. Considering the non-convex characteristic of the optimization model, cooperative game theory provides a vivid and flexible framework for collaborative detection among intelligent antennas in the RSN. The existence and uniqueness of the core are analyzed and reasoned. Furthermore, the Shapley values, derived based on the fairness principle, are utilized to address the cooperative game power allocation problem. Weighted graph techniques are employed to simplify computational complexity, resulting in a power allocation scheme that adheres to Pareto optimality and fairness principles. Numerical results validate our theoretical analysis and the effective performance of the proposed algorithm, which approaches optimal RA method with significant instantaneity.

The proliferation of electronic countermeasure (ECM) technology has presented military radar with unprecedented challenges as it remains the primary method of battlefield situational awareness. In this paper, a joint antenna scheduling and power allocation (JASPA) scheme is put forward for multi-target tracking (MTT) in the distributed multiple-input multiple-output (D-MIMO) radar. Aiming at radar resource scheduling in the presence of range deception jamming (RDJ), the false target discriminator is designed based on the Cramer–Rao lower bound (CRLB) in terms of the spoofing range, and the predicted conditional CRLB (PC-CRLB) plays a role in evaluating tracking accuracy. The JASPA scheme integrates the quality of service (QoS) principle to develop an optimization model based on false target discrimination, with the objective of enhancing both the discrimination probability of false targets and the tracking accuracy of real targets concurrently. Since the optimal variables can be separated in constraints, a four-step optimization cycle (FSOC)-based algorithm is developed to solve the multidimensional non-convex problem. Numerical simulation results are provided to illustrate the effectiveness of the proposed JASPA scheme in dealing with MTT in the RDJ environment.

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.

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.