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In 2010, Vukičević introduced an new graph invariant, the inverse sum indeg index of a graph, which has been studied due to its wide range of applications. Let Bnd be the class of bipartite graphs of order n and diameter d. In this paper, we mainly characterize the bipartite graphs in Bnd with the maximal inverse sum indeg index. Bipartite graphs with the largest, second-largest, and smallest inverse sum indeg indexes are also completely characterized.

Multi-circular SAR(MCSAR) can obtain holographic three-dimensional (3D) images of interesting observation targets, which is a significant research field at present. For anisotropic scatterers, the multi-circular SAR incoherent 3D imaging strategy combines the principle of tomography SAR inversion to obtain better reconstruction results. However, in incoherent 3D imaging, the traditional L1-norm regularization method only considers sparse representation and reconstruction in the sub-aperture pixel unit along the elevation direction, and the target structural sparsity in the same pixel unit between adjacent sub-apertures is not considered. The hierarchical sparsity constraint method in multi-circular SAR 3D target reconstruction is proposed in this paper, the L1-norm regularization is used in sub-aperture elevation, and the L2,1-regularization-based group sparsity constraint is adopted in elevation of the adjacent sub-aperture. In this paper, the sparse group thresholding iterative method is proposed to reconstruct the 3D observed target image with MCSAR data. Compared with the traditional L1-norm regularization method and IAA-GLRT method for MCSAR target reconstruction in elevation, the proposed method in this paper could obtain clearer 3D observation target images, with fewer desultory points along the elevation direction. Detailed simulation data analysis and 3D imaging processing of real MCSAR data demonstrate the advantages of the proposed method.

Synthetic aperture radar (SAR) is a widely used remote sensing observation technique. However, SAR raw echo data may be lost during the process of data acquisition by radar platform. In this paper, the imaging problem of SAR echo signal with periodically missing data along the azimuth is analyzed and a novel imaging method is proposed. Firstly, the problem of artificial artifact targets caused by periodically missing data is explained in detail, and the corresponding mathematical model is established. Then, the recovery method based on the RELAX algorithm with periodic notches data is proposed. In addition, when the size of two-dimensional (2D) echo data are large, block restoration along the azimuth is proposed to reduce the amount of calculation. Finally, the advantages of the algorithm proposed in this paper is demonstrated by the points target simulated SAR echo data processing and the real raw SAR echo data processing. When the azimuth periodically missing data rate is 50%, the SAR echo data can be recovered and the well-focused image can be obtained. Comparing the image entropy value and structural similarity index (SSIM) of the focused image, it proves the superiority of the proposed algorithm in solving the imaging problem of SAR azimuth periodically missing data.