Based on the low imaging resolution of bistatic inverse synthetic aperture radar (Bi-ISAR) and the failure of pulse correlation under the condition of sparse aperture cause that of the traditional self-focusing algorithm, a Bi-ISAR sparse aperture self-focusing algorithm with the combined constraint of image quality optimization and sparsity is proposed. First, the proposed algorithm establishes the Bi-ISAR sparse aperture self-focusing signal model, reconstructs images through fast sparse Bayesian learning (FSBL), uses the minimum Tsallis entropy and constraints the reconstruction process, iteratively updates the phase error, and performs self-focusing to realize the initial phase correction of Bi-ISAR images. Simulation results show that the proposed algorithm has a fast convergence speed, strong robustness to noise, and high accuracy in reconstructing images.

中文翻译：

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基于压缩感知和最小Tsallias熵联合约束的双基地逆合成孔径雷达稀疏孔径自聚焦算法

针对双基地逆合成孔径雷达（Bi-ISAR）成像分辨率低以及在稀疏孔径条件下脉冲相关性失效导致传统自聚焦算法存在的问题，提出了一种具有提出了图像质量优化和稀疏性的组合约束。首先，该算法建立Bi-ISAR稀疏孔径自聚焦信号模型，通过快速稀疏贝叶斯学习（FSBL）重构图像，使用最小Tsallis熵和约束重构过程，迭代更新相位误差，并进行自聚焦。聚焦实现Bi-ISAR图像的初始相位校正。仿真结果表明，该算法收敛速度快，对噪声的鲁棒性强，