However, these algorithms cannot guarantee convergence to the global optimum, which affects the reconstruction accuracy.

Greedy iterative algorithms represented by the block orthogonal matching pursuit (BOMP) algorithm are easy to implement and have low computational complexity. Block sparse reconstruction algorithms mainly include greedy iterative algorithms, convex optimization algorithms, and Bayesian framework-based algorithms. Block sparse reconstruction algorithms are utilized to achieve high-resolution two-dimensional ISAR fusion imaging of block structure targets. It is necessary to consider the block structure characteristics and the correlation of the block sparse echo signal in ISAR imaging of block structure targets. The nonzero scattering coefficients of block structure targets are considered continuously located in the imaging scene. The targets usually have some block structure characteristics in practice scenes, such as satellites and aircraft with complex structures. The multiradar observation echo data can be regarded as the sampling data, and sparse reconstruction algorithms can be utilized to achieve two-dimensional ISAR fusion imaging based on sparse representation. Since the echo data in ISAR imaging has a sparse property, the CS theory can be applied to ISAR fusion imaging. Ĭompressive sensing (CS) theory can accurately reconstruct sparse signals by using a small number of measurements, which breaks the Nyquist sampling theorem and greatly reduces the sampling number. The multiradar fusion imaging technology is utilized to fuse multiangle and multiband echo data measured by multiple radars with different frequency bands at different observation angles to improve the two-dimensional resolution of images.

In a monostatic ISAR imaging system, the range resolution and azimuth resolution are restricted by the limited transmitting bandwidth and observation angle, respectively. High-resolution inverse synthetic aperture radar (ISAR) images can provide information such as the size and geometric structure of targets, which is conducive to target recognition and classification. Finally, simulation experiments show that the proposed method can effectively achieve block sparse signal reconstruction and two-dimensional multiradar ISAR fusion imaging of block structure targets. The algorithm further accelerates the iterative convergence speed and improves the imaging efficiency by combining the weighted back-adding residual and condition number optimization of the basis matrix. Then, considering the block sparsity of the echo data of block structure targets, the FLBIUB algorithm is utilized to achieve the block sparse signal reconstruction and obtain the fusion image. The multiradar ISAR fusion imaging is transformed into a signal sparse representation problem by vectorization operation. First, the ISAR imaging echo data of block structure targets is established based on the geometrical theory of the diffraction model. To improve the two-dimensional resolution of inverse synthetic aperture radar (ISAR) imaging, a fast linearized Bregman iteration for unconstrained block sparsity (FLBIUB) algorithm is proposed to achieve multiradar ISAR fusion imaging of block structure targets. The range resolution and azimuth resolution are restricted by the limited transmitting bandwidth and observation angle in a monostatic radar system.

Super vectorizer 1.60