论文标题
通过稀疏的贝叶斯学习向SAR层析成像倒置
Towards SAR Tomographic Inversion via Sparse Bayesian Learning
论文作者
论文摘要
现有的SAR层析成像（Tomosar）算法主要基于SAR成像模型的反转，这些模型通常在计算上昂贵。先前的研究表明，使用KPCA（例如KPCA）分解信号并降低计算复杂性的观点。本文为基于稀疏的贝叶斯学习的新数据驱动方法提供了初步演示。模拟数据上的实验表明，所提出的方法在估计散射器的转向向量方面明显胜过KPCA方法。这给出了数据驱动方法的透视图，或将其与模型驱动的方法相结合，以进行高精度的层析成像反转。
Existing SAR tomography (TomoSAR) algorithms are mostly based on an inversion of the SAR imaging model, which are often computationally expensive. Previous study showed perspective of using data-driven methods like KPCA to decompose the signal and reduce the computational complexity. This paper gives a preliminary demonstration of a new data-driven method based on sparse Bayesian learning. Experiments on simulated data show that the proposed method significantly outperforms KPCA methods in estimating the steering vectors of the scatterers. This gives a perspective of data-drive approach or combining it with model-driven approach for high precision tomographic inversion of large areas.
