Efficient Image Reconstruction Methods Based on Structured Sparsity for Short-Range Radar

The radar imaging method, based on matched filtering (MF), generates high gratings and sidelobes in sparse aperture data, resulting in artifacts in the radar image. The theory of compressed sensing (CS) has brought a breaking change to radar imaging, and imaging enhancement can be realized by exploiting the sparsity of the target image. However, traditional sparse imaging methods ignore the correlation between scatterers. This leads to difficulties in accurately extracting the target’s shape contour and structural features. Thus, in this article, a convolutional reweighted model based on structured sparsity features is proposed. Specifically, a dynamically relaxing threshold is achieved through the convolutional reweighted
$\boldsymbol {l}_{1}$
norm, promoting the sparsity of clustered structures in radar images. Furthermore, to avoid large-scale matrix inversion, the issue is, respectively, addressed through the alternating direction method of multipliers (ADMMs) joint gradient descent framework and linearization approximation approach. In addition, the priori information of MF is utilized to adaptively update the imaging support set during the iteration process, aiming to reduce the data storage pressure. Finally, a large number of simulation and experimental results confirm the generality of the proposed algorithms for radar data in different frequency bands, as well as their superiority in terms of computational efficiency and image quality.