论文标题
部分可观测时空混沌系统的无模型预测
Optimizing Sensing Matrices for Spherical Near-Field Antenna Measurements
论文作者
论文摘要
在本文中，我们解决了通过使用压缩传感（CS）来减少球形近场天线测量（SNF）所需样品数量的问题。确保稀疏恢复算法的数值性能的条件是具有低相干性的传感矩阵的设计。在不固定采样模式的任何部分的情况下，我们提出的采样点可以通过使用增强的拉格朗日方法来最大程度地减少相应传感矩阵的相互连贯性。数值实验表明，与其他已知的采样模式（例如螺旋和Hammersley采样方案）相比，提出的采样方案在相变图方面取得了更高的恢复成功。此外，我们还证明，具有优化传感矩阵的CS的应用比重建球形模式系数（SMC）和远场模式的经典方法所需的样本少。
In this article, we address the problem of reducing the number of required samples for Spherical Near-Field Antenna Measurements (SNF) by using Compressed Sensing (CS). A condition to ensure the numerical performance of sparse recovery algorithms is the design of a sensing matrix with low mutual coherence. Without fixing any part of the sampling pattern, we propose sampling points that minimize the mutual coherence of the respective sensing matrix by using augmented Lagrangian method. Numerical experiments show that the proposed sampling scheme yields a higher recovery success in terms of phase transition diagram when compared to other known sampling patterns, such as the spiral and Hammersley sampling schemes. Furthermore, we also demonstrate that the application of CS with an optimized sensing matrix requires fewer samples than classical approaches to reconstruct the Spherical Mode Coefficients (SMCs) and far-field pattern.
