A new inverse synthetic aperture radar (ISAR) imaging framework is proposed to obtain high cross-range resolution under sparse aperture conditions, which is a challenge when the signal-to-noise ratio is low. Motivated by the sparsity and low rank of target’s 2-D distribution, the imaging problem is converted to the simultaneously sparse and low-rank signal matrix reconstruction problem under multiple measurement vector (MMV) model, and a novel reconstruction method based on joint constraints of sparsity and low rank is proposed. Due to the over-relax problem, the traditional convex optimization method cannot achieve a better performance using joint structures than exploiting just one of the constraints. As such, a nonconvex penalty function is introduced. To avoid the local minima, the convexity of the cost function should be ensured when constructing the nonconvex penalty function. The adaptive filtering framework, which is a powerful way to recovery the sparse low-rank matrix accurately from its noisy observation, is adopted as a reconstruction algorithm. Furthermore, the optimal step size formula and the idea of smoothed zero norm are used to enhance the convergence and the ability to suppress noise. The newly proposed method is verified by the simulation experiment, which has a better performance in image quality, robustness to noise, and imaging speed.

中文翻译：

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基于稀疏度和低秩联合约束的稀疏孔径ISAR成像方法

提出了一种新的逆合成孔径雷达（ISAR）成像框架，以在稀疏孔径条件下获得较高的跨范围分辨率，这在信噪比较低时是一个挑战。由于目标二维分布的稀疏性和低秩，在多测量矢量（MMV）模型下，将成像问题转换为同时稀疏和低秩的信号矩阵重构问题，并提出了一种基于联合约束的新颖重构方法。建议稀疏和低等级。由于过度松弛问题，传统的凸优化方法使用关节结构无法获得比仅利用约束条件之一更好的性能。这样，引入了非凸罚函数。为了避免局部最小值，在构造非凸罚函数时，应确保成本函数的凸性。自适应滤波框架被用作重构算法，该框架是从嘈杂的观测中准确地恢复稀疏低秩矩阵的有效方法。此外，使用最佳步长公式和平滑零范数的思想来增强收敛性和抑制噪声的能力。仿真实验验证了该新方法的有效性，该方法在图像质量，抗噪性和成像速度方面都有较好的表现。最佳步长公式和平滑零范数的思想用于增强收敛性和抑制噪声的能力。仿真实验验证了该新方法的有效性，该方法在图像质量，抗噪性和成像速度方面都有较好的表现。最佳步长公式和平滑零范数的思想用于增强收敛性和抑制噪声的能力。仿真实验验证了该新方法的有效性，该方法在图像质量，抗噪性和成像速度方面都有较好的表现。