Based on the assumption of isotropic scattering, a wider angle aperture can provide improved resolution of synthetic aperture radar images. However, due to the limited angular persistence of scatterers for high-resolution terahertz radar, traditional imaging techniques make aspect-dependent scatterers blur. In this paper, a novel wide-angle circular synthetic aperture radar (CSAR) imaging technique is investigated on the basis of the adaptive subaperture partition method in the terahertz band, with the objective to enhance object reconstruction. The proposed algorithm captures the scattering characteristics of aspect-dependent scatterers, which is applied to adaptively divide the full aperture into multiple subapertures. Because the subapertures are approximately consistent with the scattering persistence angles of aspect-dependent scatterers, the real reflectivity characteristics of objects are better kept in the composite two-dimensional CSAR images obtained with the noncoherent combination of the subaperture images on the basis of the generalized likelihood ratio test imaging approach. Real data results have verified the effectiveness of the proposed algorithm.

In response to the increasing threat of terrorism, standoff security screening of person-borne concealed weapons or contraband in public areas, such as airports and railway stations, is an urgent requirement; however, current solutions suffer from the limited field of view and the problem of security screening with interfering with the passenger flow. This paper proposes an imaging method that exploits the angular diversity of distributed multistatic terahertz (THz) radar network in combination with inverse synthetic aperture radar (ISAR) technique to realize high-resolution penetration imaging at a standoff range. The radar network is based on multistatic ISAR systems, and the multistatic ISAR system’s equivalent monostatic model is derived. According to the equivalent monostatic model, we develop the image formation algorithm and give the analytical expression of the cross-range resolution. Compared with conventional monostatic ISAR configuration, the analytical expression shows that the multistatic ISAR configuration can enhance the image’s cross-range resolution and the maximum enhanced factor is approximately equal to the number of radars in the multistatic system. Numerical simulations and experimental results agree well with the theoretical derivation. The proposed imaging approach offers a low-cost and flexible alternative to the present systems not only for security screening but also for noncooperative target imaging.

The rising population of space debris poses an enormous threat to all space vehicles, including space shuttles and other spacecraft with operators aboard; and their detection, tracking, and identification are of great importance. Using the spinning motion and the translational motion component that is parallel to its major axis, we have established a 3-D spiral synthetic aperture radar (SAR) imaging geometry and its corresponding signal model for space debris in this letter. Spatial offsets and coupling in the along-track and altitude directions, caused by the spiral motion of the radar, are compensated in the frequency domain. With these compensation procedures, the spiral SAR is simplified as a cylindrical scan mode. Then, a 3-D wavenumber domain algorithm is proposed to realize coherent imaging. The real data processing results are presented to demonstrate the effectiveness of the proposed algorithm for imaging space debris.

The high-resolution inverse synthetic aperture radar (ISAR) imaging of the maneuvering target with small-angle measurements is expected, which can be achieved by using the radar operating at a higher central frequency. However, because the defocus induced by the spatially variant phase error becomes significant with the resolution improvement and the central frequency increasing, the global phase correction is invalid. Moreover, due to the scattering characteristics of aspect-dependent scatterers and small-angle measurements, we can rarely obtain the rough outline of the maneuvering target. To deal with these difficulties, we propose an ISAR imaging method for the maneuvering target with distributed high resolution radars. To mitigate the spatially variant phase error, we correct phase errors in patches based on the weighted least-squares algorithm, which makes the image well-focused. Then, the ISAR imaging method with distributed radars is presented to obtain the fusion image, with the object to capture enough scatterers to represent the maneuvering target. The fusion image could provide more details. Real data have verified the effectiveness of the proposed method.

Clock bias and drift between the unknown source and sensors affect the localization performance when using hybrid time of arrival (TOA) and frequency of arrival (FOA) measurements. A recent study proposed a semidefinite programming (SDP) solution for estimating motion parameters, including position and velocity. However, this approach is computationally intensive and suffers from numerical problems. This paper introduces a projection onto the null space of the redundant variable vector, which alleviates the numerical issues and leads to a closed-form solution. Since the projection does not incorporate the constraints between the redundant variables and the unknown motion parameters, a correction stage is required to improve accuracy. Two correction approaches have been developed: one based on constraints and the other on first-order Taylor expansion. These result in two fast and effective solutions: Projection with Correction from Constraints (Proj-C) and Projection with Correction from Taylor Expansion (Proj-TE). The proposed solutions achieve Cramér-Rao lower bound (CRLB) performance levels in both theory and simulation, provided the noise is mild. We also consider the uncertainty of sensor positions and velocities, extending the proposed solutions to mitigate accuracy loss. Numerical results show that Proj-C has better mean-square-error (MSE) performance for velocity and clock drift estimation than Proj-TE when the noise/error is relatively large, while the latter significantly reduces bias to the maximum likelihood estimation (MLE) level. Both proposed solutions avoid degradation in the small noise/error region due to the relaxation in SDP.

Localizing a moving source by Time Difference of Arrival (TDOA) and Frequency Difference of Arrival (FDOA) commonly requires at least $N+1$ sensors in N-dimensional space to obtain more than N pairs of TDOAs and FDOAs, thereby establishing more than $2N$ equations to solve for $2N$ unknowns. However, if there are insufficient sensors, the localization problem will become underdetermined, leading to non-unique solutions or inaccuracies in the minimum norm solution. This paper proposes a localization method using TDOAs and FDOAs while incorporating the motion model. The motion between the source and sensors increases the equivalent length of the baseline, thereby improving observability even when using the minimum number of sensors. The problem is formulated as a Maximum Likelihood Estimation (MLE) and solved through Gauss–Newton (GN) iteration. Since GN requires an initialization close to the true value, the MLE is transformed into a semidefinite programming problem using Semidefinite Relaxation (SDR) technology, while SDR results in a suboptimal estimate, it is sufficient as an initialization to guarantee the convergence of GN iteration. The proposed method is analytically shown to reach the Cramér–Rao Lower Bound (CRLB) accuracy under mild noise conditions. Simulation results confirm that it achieves CRLB-level performance when the number of sensors is lower than $N+1$, thereby corroborating the theoretical analysis.