论文标题
一种新型的Tomosar成像方法，几乎没有基于嵌套阵列的观测
A novel TomoSAR imaging method with few observations based on nested array
论文作者
论文摘要
合成孔径雷达层析成像（Tomosar）基线优化技术能够降低系统的复杂性并提高数据的时间连贯性，这已成为Tomosar领域的一项重要研究。在本文中，我们提出了一种嵌套的Tomosar技术，该技术将嵌套的阵列引入Tomosar作为基线配置。该技术通过嵌套阵列获得均匀且连续的差异共阵列，以增加系统的自由度（DOF），并沿高度方向扩展了虚拟孔径。为了充分利用差异共同阵列，需要获得回声的协方差矩阵。因此，我们提出了基于嵌套阵列的Tomosar稀疏重建算法，该算法使用自适应协方差矩阵估计来改善复杂场景中的估计性能。我们通过模拟和真实的数据实验证明了该方法的有效性。与传统的Tomosar和Coprime Tomosar相比，我们提出的方法的成像结果具有更好的反噪声性能，并保留了更多图像信息。
Synthetic aperture radar tomography (TomoSAR) baseline optimization technique is capable of reducing system complexity and improving the temporal coherence of data, which has become an important research in the field of TomoSAR. In this paper, we propose a nested TomoSAR technique, which introduces the nested array into TomoSAR as the baseline configuration. This technique obtains uniform and continuous difference co-array through nested array to increase the degrees of freedom (DoF) of the system and expands the virtual aperture along the elevation direction. In order to make full use of the difference co-array, covariance matrix of the echo needs to be obtained. Therefore, we propose a TomoSAR sparse reconstruction algorithm based on nested array, which uses adaptive covariance matrix estimation to improve the estimation performance in complex scenes. We demonstrate the effectiveness of the proposed method through simulated and real data experiments. Compared with traditional TomoSAR and coprime TomoSAR, the imaging results of our proposed method have a better anti-noise performance and retain more image information.
