The earlier works in the context of sparsity-driven SAR imaging have shown significant improvement in the reconstruction process due to admitting sparsity as a prior. In spite of the importance of real-time processing requirement in the remote sensing (RS) applications, most of the works have not focused on real-time procedures and reducing the computational burden, but rather enhancing the quality of formed image. To address this weakness, this paper presents a problem-driven algorithm, which relies on Majorization–Minimization (MM) procedure. Using MM in our solutions, a simpler surrogate optimization problem is solved instead of the difficult original form. To show the efficacy of MM algorithm in real-time applications experimental results based on simulated and real data along with a performance analysis are presented. All results validate the superiority of the proposed MM-based method in terms of computational load and processing time as compared with previous works.

稀疏驱动SAR成像的早期工作表明，由于优先考虑稀疏性，因此重建过程得到了显着改善。尽管实时处理要求在遥感（RS）应用中很重要，但大多数工作并未集中在实时过程和减少计算负担上，而是提高了所形成图像的质量。为了解决这一弱点，本文提出了一种基于问题的算法，该算法依赖于“最小化-最小化”（MM）过程。在我们的解决方案中使用MM，可以解决一个更简单的替代优化问题，而不是困难的原始形式。为了展示MM算法在实时应用中的有效性，提出了基于模拟和真实数据的实验结果以及性能分析。