The inverse synthetic aperture radar (ISAR) imaging technique of a 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 2D 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 2D 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 have a low-rank 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.

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.

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.