Inverse Synthetic Aperture Radar (ISAR) imaging technique of moving target with sparse sampling data has attracted wide attention, due to its ability to reduce the data collection burden. However, traditional low-rank or 2-D Compressive Sensing (CS) based ISAR imaging methods can handle the random sampling or the separable sampling data only. When the specific data collection condition cannot be satisfied, low-rank or 2-D CS based methods cannot provide satisfactory imaging results any more. To remedy this problem, in this paper, we proposed a joint low-rank and sparsity priors constrained model for ISAR imaging with various sparse data patterns. This model is inspired by the facts that the received radar data has a lowrank property and the ISAR image is sparse on the specific dictionary. Two reconstruction algorithms to solve the double priors constrained optimization problem are developed under the alternative direction method of multipliers (ADMM) framework with the help of augmented Lagrange multipliers (ALM). Results on simulation data and real data show that the proposed methods are quite effective in recovering missing samples and focused image, and perform better than the matrix completion based method and the sparse representation based method when dealing with the various kinds of sparse sampling data.

A 2-D range/cross-range radar image of a target is always sparse since only a few strong scattering centers occupy the whole image plane, and thus, it is quite suitable to apply the compressive sensing (CS) theory to obtain inverse synthetic aperture radar (ISAR) images. In this paper, a novel fully polarimetric ISAR imaging method based on CS is proposed. First, a definition of joint sparsity is given by exploiting the scattering characteristics of a target in fully polarimetric channels. Then, fully polarimetric ISAR images are constructed by means of the sparse recovery algorithm under the constraint of the joint sparsity. This proposed imaging method combines the merits of a full-polarization technique and CS theory, and hence, it has two main advantages: it can provide high-resolution ISAR images with limited measurements, which is a promising technique for reducing data storage; it generates fully polarimetric ISAR images with the number and the positions of the scattering centers aligned in polarimetric channels, which allows for further polarimetric scattering characteristic analysis. Finally, both simulation and experimental results are shown to demonstrate the validity of the proposed approach.

Inverse synthetic aperture radar (ISAR) images can be obtained using digital video broadcasting-terrestrial (DVB-T)-based passive radars. However, television broadcast-transmitted signals offer poor range resolution for imaging purposes, because they have a narrower bandwidth with respect to those transmitted by a dedicated ISAR system. To reach finer range resolutions, signals composed of multiple DVB-T channels are required. Problems arise, however, because DVB-T channels are typically widely separated in the frequency domain. The gaps between channels produce high grating lobes in the image domain when Fourier-based algorithms are used to form the ISAR image. In this paper, compressive sensing theory is investigated to address this problem because of its ability to reconstruct sparse signals by using incomplete measures. By solving an optimization problem under the constraint of signal sparsity, passive ISAR images can be obtained with strongly reduced grating lobes. Both simulation and experimental results are shown to demonstrate the validity of the proposed approach.

Inverse synthetic aperture radar (ISAR) can form two‐dimensional (2D) electromagnetic images of a target, but it cannot provide the third dimensional information about the target. Conventional 3D turntable ISAR imaging requires data collection over densely azimuth‐elevation samples, which needs a large amount of data storage. In this study, an effective 3D ISAR imaging algorithm for turntable model based on compressive sensing is proposed, which exploits the sparsity in the image domain to achieve 3D reconstruction by using a limited number of measurements. Firstly, the 3D data tensor is converted into a 2D matrix by stacking slices of data along one specific dimension; then a 2D optimisation reconstruction approach is applied to solve a sparsity‐driven optimisation problem to obtain the 2D distribution of the scatterers. Lastly, 3D ISAR images are generated by rearranging the scatterer distribution in the 2D map into a 3D volume. This imaging scheme only needs a small number of measurements, and reduces the required memory and computational burden significantly. Simulation results are finally shown to validate the proposed algorithm.

In this letter, we propose a fast reconstruction algorithm for 3-D turntable microwave imaging from sparse measurements. A conventional Fourier-transform-based 3-D microwave imaging method collects data over densely azimuth-elevation samples and needs a large amount of data storage and long collection time. To reduce the cost of data acquisition, the proposed method exploits the sparsity in the image domain to achieve 3-D microwave imaging by utilizing sparse measurements. For this aim, the signal model is first represented as a tensor array, and then, a novel sparse reconstruction algorithm called 3-D-SL0 is applied to recover the 3-D scattering reflectivity, i.e., a 3-D image. Simulation results are finally shown to investigate the validity of the proposed method.