Inverse synthetic aperture radar (ISAR) imaging of a precessing target, which is a kind of fast spinning target, is faced with migration through range cell when using traditional imaging algorithms. Theory of compressed sensing (CS) suggests that exact recovery of an unknown sparse signal with an overwhelming probability can be achieved from very limited number of samples. A cycle shift smoothed L0 algorithm based on CS is proposed in this paper for high-resolution ISAR imaging of precessing targets by exploiting sparse apertures. A precessing cone-shaped target model is built and a 3-D reconstruction method based on multistatic ISAR is proposed. Simulations and electromagnetic computation verify the validity of the proposed method.

Space-time adaptive processing (STAP) based on sparse recovery (SR-STAP) techniques exhibits significantly better performance than conventional STAP algorithms within a very small number of snapshots. However, when the clutter patches do not locate exactly on the discrete space-time grid points, the performances of SR-STAP algorithms degrade severely. In this letter, a low-complexity off-grid STAP algorithm based on local search clutter subspace estimation is proposed to overcome this issue. In the proposed algorithm, the global atoms are first selected from the reduced-dimension global STAP dictionary using the design selection criterion. Then, the optimal atoms are searched from the local STAP dictionary. Finally, these space-time steering vectors corresponding to the optimal atoms are used to construct the clutter subspace iteratively, and the STAP weight is obtained by projecting the snapshot on the subspace orthogonal to the clutter subspace. Numerical experiments with both simulated and Mountain-Top data are carried out to demonstrate the effectiveness of the proposed algorithm.

In actual condition, array elements deficiency or transmission errors lead to incomplete data, which is called sparse aperture (SA) data. In inverse synthetic aperture radar (ISAR) imaging, this large‐gaped data produces poor‐quality ISAR images when using traditional range–Doppler algorithm. Recently, imaging algorithms based on compressed sensing (CS) theory alleviate this problem effectively because CS theory indicates that sparse signal can be reconstructed from incomplete measurements. However, the basis mismatch problem in CS‐based algorithms may degrade the ISAR image. In this study, a reweighted atomic‐norm minimisation (ANM) (RAM)‐based imaging method is proposed. RAM is a gridless sparse method, which can enhance sparsity and resolution. RAM formulates an optimisation problem and iteratively carries out ANM with a sound reweighting strategy. By reformulating the RAM as a semi‐definite programme, the echoes with full aperture (FA) are reconstructed from SA data. After that, ISAR imaging with the reconstructed FA data is achieved via the conventional azimuth compression method. Simulated and real data results demonstrate the effectiveness and superiority of the proposed method.

Recently, compressed sensing (CS) methods are widely used in high‐resolution inverse synthetic aperture radar (ISAR) imaging. However, these CS‐based imaging methods generally do not take the block‐sparse structure of the ISAR images into account, and the image recovery performance needs to be improved. By utilising the block‐sparse structure of the signal, more sparse solution and better focused ISAR images can be obtained. In this study, the authors convert the block‐sparse signal recovery problem into a sparse recovery problem for multiple measurement vector (MMV), which can be solved more efficiently. A sparser and more accurate solution can be obtained based on the MMV model, and therefore better focused ISAR images can be recovered. Simulation and experimental results validate the effectiveness of the proposed method.

Owing to the sparsity of the space distribution of point scatterers, compressed sensing (CS) method is successfully applied in inverse synthetic aperture radar (ISAR) imaging. However, in addition to sparsity, ISAR images usually exhibit group sparse structure. Here, the authors propose a novel two‐dimensional (2D) group primal dual active set with continuation (2DGPDASC) algorithm to recover an ISAR image, which always exhibit 2D group sparse structure. This algorithm is based on the regularised least‐squares problem with an penalty model. At each iteration of the proposed method, it involves solving a least‐squares problem on the active set only, and exhibits a fast local convergence within a finite step. Experimental results validate the effectiveness and superiority of the proposed method.