论文标题
基于加速的原子规范最小化的无网状断层扫描SAR成像并效率
Gridless Tomographic SAR Imaging Based on Accelerated Atomic Norm Minimization with Efficiency
论文作者
论文摘要
合成孔径雷达（SAR）断层扫描（Tomosar）可以基于对同一场景的多个二维（2D）观测值的重建和三维（3D）定位。沿着海拔方向的解决方案可以视为线光谱估计问题。但是，传统的超分辨率频谱估计算法需要多个快照和不相关的目标。同时，作为现代最受欢迎的Tomosar成像方法，基于压缩传感的方法（CS）方法遭受格栅不匹配效应，这显着降低了成像性能。作为一种无网的CS方法，原子规范最小化可以避免网格效果，但需要巨大的计算资源。 Addressing the above issues, this paper proposes an improved fast ANM algorithm to TomoSAR elevation focusing by introducing the IVDST-ANM algorithm, which reduces the huge computational complexity of the conventional time-consuming semi-positive definite programming (SDP) by the iterative Vandermonde decomposition and shrinkage-thresholding (IVDST) approach, and retains the benefits of ANM在无网状成像和单个快照恢复方面。我们使用模拟数据进行了实验，以评估所提出的方法的性能，并还提供了来自SARMV3D成像1.0数据集的城市区域的重建结果。
Synthetic aperture radar (SAR) tomography (TomoSAR) enables the reconstruction and three-dimensional (3D) localization of targets based on multiple two-dimensional (2D) observations of the same scene. The resolving along the elevation direction can be treated as a line spectrum estimation problem. However, traditional super-resolution spectrum estimation algorithms require multiple snapshots and uncorrelated targets. Meanwhile, as the most popular TomoSAR imaging method in modern years, compressed sensing (CS) based methods suffer from the gridding mismatch effect which markedly degrades the imaging performance. As a gridless CS approach, atomic norm minimization can avoid the gridding effect but requires enormous computing resources. Addressing the above issues, this paper proposes an improved fast ANM algorithm to TomoSAR elevation focusing by introducing the IVDST-ANM algorithm, which reduces the huge computational complexity of the conventional time-consuming semi-positive definite programming (SDP) by the iterative Vandermonde decomposition and shrinkage-thresholding (IVDST) approach, and retains the benefits of ANM in terms of gridless imaging and single snapshot recovery. We conducted experiments using simulated data to evaluate the performance of the proposed method, and reconstruction results of an urban area from the SARMV3D-Imaging 1.0 dataset are also presented.
