Abstract. A sparse representation-based bistatic inverse synthetic aperture radar (ISAR) imaging method can achieve a high-resolution image of a target with sparse aperture data. However, the bistatic ISAR system is more sensitive to noise than the monostatic one because of its nonmirror reflection geometry. To overcome this drawback, we propose the sparse aperture bistatic ISAR imaging method based on joint sparse model. Considering the joint sparse information of bistatic ISAR echo, a joint sparse imaging model is constructed. Then, the dechirped sparse aperture bistatic ISAR echo after translational compensation is transformed into range fast time and azimuth slow time domains by the joint sparse imaging model, and a corresponding azimuth sparse basis is constructed. Then a joint sparse complex approximate message passing algorithm is proposed to joint sparse imaging model. The joint sparse imaging problem is converted to a block sparse imaging problem by vectorization. Using the relationship between the vectorization of the matrix and the Kronecker product, a matrix iteration structure is proposed to solve the joint sparse model efficiently and accurately. The experimental results based on both scattering point model and electromagnetic calculation model data verify the effectiveness of the proposed imaging method.

中文翻译：

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低信噪比条件下的稀疏孔径双基地ISAR成像

摘要。一种基于稀疏表示的双基地逆合成孔径雷达（ISAR）成像方法可以利用稀疏孔径数据实现目标的高分辨率图像。然而，由于其非镜面反射几何结构，双基地 ISAR 系统比单基地系统对噪声更敏感。针对这一缺点，我们提出了基于联合稀疏模型​​的稀疏孔径双基地ISAR成像方法。考虑双基地ISAR回波的联合稀疏信息，构建联合稀疏成像模型。然后，通过联合稀疏成像模型将平移补偿后的解啾稀疏孔径双基地ISAR回波转化为距离快时域和方位慢时域，并构建相应的方位稀疏基。然后提出一种联合稀疏复数近似消息传递算法来建立联合稀疏成像模型。通过向量化将联合稀疏成像问题转化为块稀疏成像问题。利用矩阵向量化与Kronecker积之间的关系，提出了一种矩阵迭代结构来高效准确地求解联合稀疏模型​​。基于散射点模型和电磁计算模型数据的实验结果验证了所提出的成像方法的有效性。提出了一种矩阵迭代结构来高效准确地求解联合稀疏模型​​。基于散射点模型和电磁计算模型数据的实验结果验证了所提出的成像方法的有效性。提出了一种矩阵迭代结构来高效准确地求解联合稀疏模型​​。基于散射点模型和电磁计算模型数据的实验结果验证了所提出的成像方法的有效性。