Compressed sensing (CS) based imaging technique is considered to be an effective solution for high-resolution radar imaging due to the sparse distribution of the scatterers. Nevertheless, the unoptimized CS based synthetic aperture radar (SAR) or inverse SAR (ISAR) imaging approach may suffer from the computationally intensive problem when apply it to wideband radar signatures of electrical large-scale targets. In this paper, a two-dimensional (2-D) superresolution imaging technique based on peak search and compressed sensing (PS-CS) is presented. A peak search strategy is first developed to solve the problem of high computational complexity of CS based method in scattering parameter estimation. Superresolution imaging result is then achieved by extrapolating the estimated parameters of scattering along with the observing angle dimension and frequency dimension. The numerical and measurement data acquired from different man-made targets are presented to demonstrate the feasibility and usefulness of the proposed technique for supperresolution radar imaging.

The 3-D inverse synthetic aperture radar (ISAR) tomography is an enabling technique for applications such as the exact diagnosis of scattering mechanisms for complex targets. Nevertheless, current ISAR tomography solutions still suffer from problems such as the great computational complexity and optimal utilization of the signatures acquired from limited baselines. In this work, we propose a fast ISAR tomography technique for fully polarimetric 3-D imaging of man-made targets. A stack of 2-D complex-valued images with different baselines and polarizations is first obtained through a phase error calibration (PEC) process and graphic processing unit accelerated polarimetric filtered backprojection. A polarimetric state-space decomposition (P-SSD) algorithm is then developed which could provide joint 3-D reconstruction results with low computational complexity. Examples from both numerical multibaseline data for the Sandia laboratories implementation of cylinders (SLICY) benchmark model and the outdoor range dataset collected by the Georgia Tech Research Institute (GTRI) are presented to demonstrate the superior performance and the usefulness of the proposed technique.

In this work, we focus on sparse representation of two-dimensional (2-D) radar signatures for man-made targets. Based on the damped exponential (DE) model, a 2-D augmented state–space approach (ASSA) is proposed to estimate the parameters of scattering centers on complex man-made targets, i.e., the complex amplitudes and the poles in down-range and aspect dimensions. An augmented state–space approach is developed for pole estimation of down-range dimension. Multiple-range search strategy, which applies one-dimensional (1-D) state–space approach (SSA) to the 1-D data for each down-range cell, is used to alleviate the pole-pairing problem occurring in previous algorithms. Effectiveness of the proposed approach is verified by the numerical and measured inverse synthetic aperture radar (ISAR) data.

In the past decade, design of sparse planar arrays has increasingly become a focal point, driven by its superior performance in terms of degrees of freedom (DOFs) and anti-mutual-coupling effects compared to other uniform distributed planar arrays. In this paper, an innovative sparse planar array geometry is introduced for two-dimensional (2-D) direction of arrival (DOA) estimation, leveraging the difference co-array (DCA) technique to generate a virtual array with large consecutive co-arrays. At first, we propose an array structure referred to as shift-based symmetric coprime planar array (SSCPA). This design acts as a springboard, utilizing shifted coprime arrays to provide a higher uniform DOFs (uDOFs) than existing structures. To address the inherent contradiction between the high uDOFs and the low mutual coupling effect of SSCPA's parameter selection, we propose an enhanced version named improved shift-based symmetric coprime planar array (ISSCPA), holding smaller weight functions for near separated sensors. This improved design maintains the same uDOFs as offered by SSCPA while improving robustness against mutual coupling. Compared with existing array structures, simulation results confirm that our proposed structure performs better in terms of resolution and accuracy for 2-D DOA estimation.

Modern man-made targets with high-speed movement capabilities are often designed as special streamlined structures. As a result, when illuminated by radar, the surface of such targets will produce a sliding scattering center whose position changes with the radar line of sight. In this paper, we present a feature extraction technique for this kind of scattering centers. The trajectory of the equivalent scattering points in the one-dimensional (1-D) high-resolution range profile (HRRP) sinogram is first predicted by utilizing the special geometric characteristics of sliding scatterers. The parameters of sliding scattering features are then extracted by combining the basis pursuit algorithm with the predicted trajectory. Examples from both numerical and measured data are presented to demonstrate the usefulness of the proposed technique.

Multi-baseline circular synthetic aperture radar (C-SAR) tomography is an important three-dimensional (3-D) radar imaging mode since it allows for omni-directional 3-D reconstruction of targets. Typically, this imaging mode splits the full-aperture data into multiple narrow-apertures to be processed separately due to the sensitivity to elevation angle and imaging height. However, the repeated one-dimensional (1-D) elevation inversion of all imaged pixels for each sub-aperture also leads to more processing time and more parameter estimation uncertainties. In this paper, a new C-SAR tomography framework based on scattering phase correction (SPC) is presented. Our main idea is to use 1-D elevation inversion to estimate the exact height of the distorted scattering points in two-dimensional (2-D) full-aperture image, and derive the imaging height transformation formula. Then these distorted scattering points of different heights are transformed to the proper heights respectively. As a result, the elevation inversion only needs to be done once for the whole framework and does not need to be done for each sub-aperture. Besides, a combination of two separate processing chains (i.e., fast coherent imaging and slices transform imaging) is used to minimize the 3-D reconstruction errors caused by the imaging height transformation and 1-D elevation inversion. Numerical and outdoor measurement results of real-world complex targets are presented to demonstrate the usefulness of the proposed framework.