The two-dimensional (2D) resolution is poor due to the narrow transmitting bandwidth and the limited observation angle in monostatic ISAR imaging. A multiradar fusion imaging method based on fast linearized Bregman iteration (FLBI) algorithm is proposed to improve the 2D resolution of the ISAR imaging. First, the sparsity of the ISAR imaging echo data is exploited to establish the multiradar fusion ISAR imaging model based on sparse representation, which can be converted into a one-dimensional sparse vector reconstruction problem. Then, a sparse reconstruction method based on FLBI is proposed to solve the sparse representation problem with large scales and achieve the ISAR fusion imaging. Combined with the weighted back-adding residual and condition number optimization of the sensing matrix, the FLBI algorithm can further accelerate the iterative convergence speed. The proposed algorithm only involves matrix–vector multiplications and componentwise shrinkages, which greatly improves the imaging efficiency. Finally, the simulation results show that the proposed method can effectively improve the iterative convergence speed and achieve the better 2D ISAR fusion imaging.

中文翻译：

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基于快速线性Bregman迭代算法的二维多雷达ISAR融合成像

二维（2D）分辨率差，这是因为单带宽ISAR成像中的发射带宽窄且观察角度有限。提出了一种基于快速线性Bregman迭代（FLBI）算法的多雷达融合成像方法，以提高ISAR成像的二维分辨率。首先，利用ISAR成像回波数据的稀疏性，建立基于稀疏表示的多雷达融合ISAR成像模型，并将其转化为一维稀疏向量重构问题。然后，提出了一种基于FLBI的稀疏重构方法，以解决大规模稀疏表示问题，实现ISAR融合成像。结合加权矩阵的加权后加残差和条件数优化，FLBI算法可以进一步加快迭代收敛速度。所提出的算法仅涉及矩阵向量乘法和按分量缩小，从而大大提高了成像效率。最后，仿真结果表明，该方法可以有效提高迭代收敛速度，达到较好的二维ISAR融合成像效果。