Li Guangtao, Xin Dongjin, Li Weixin, Yang Lei, Wang Dong, Zhou Yongkang
School of Information Science and Engineering, University of Jinan, Jinan 250022, China.
Shandong Provincial Key Laboratory of Ubiquitous Intelligent Computing, Jinan 250022, China.
Sensors (Basel). 2024 Oct 3;24(19):6418. doi: 10.3390/s24196418.
Compressed Sensing SAR Imaging is based on an accurate observation matrix. As the observed scene enlarges, the resource consumption of the method increases exponentially. In this paper, we propose a weighted -norm regularization SAR imaging method based on approximate observation. Initially, to address the issues brought by the precise observation model, we employ an approximate observation operator based on the Chirp Scaling Algorithm as a substitute. Existing approximate observation models typically utilize ( = 1, 1/2)-norm regularization for sparse constraints in imaging. However, these models are not sufficiently effective in terms of sparsity and imaging detail. Finally, to overcome the aforementioned issues, we apply regularization, which aligns with the natural image gradient distribution, and further constrain it using a weighted matrix. This method enhances the sparsity of the algorithm and balances the detail insufficiency caused by the penalty term. Experimental results demonstrate the excellent performance of the proposed method.
压缩感知合成孔径雷达（SAR）成像基于精确的观测矩阵。随着观测场景的扩大，该方法的资源消耗呈指数级增长。在本文中，我们提出了一种基于近似观测的加权范数正则化SAR成像方法。首先，为了解决精确观测模型带来的问题，我们采用基于Chirp缩放算法的近似观测算子作为替代。现有的近似观测模型通常在成像中利用（ = 1, 1/2）范数正则化进行稀疏约束。然而，这些模型在稀疏性和成像细节方面不够有效。最后，为了克服上述问题，我们应用与自然图像梯度分布一致的正则化，并使用加权矩阵对其进一步约束。该方法提高了算法的稀疏性，并平衡了由惩罚项导致的细节不足。实验结果证明了所提方法的优异性能。
