论文标题
基于离网压缩感应的低复杂性稀疏阵列合成
Low-complexity Sparse Array Synthesis Based on Off-grid Compressive Sensing
论文作者
论文摘要
提出了一种新型的稀疏阵列合成方法，用于非均匀平面阵列，该方法属于压缩传感（CS）基于基于压缩感的系统。特别是，我们提出了一种离网精炼技术，以同时优化具有低复杂性的天线元件位置和激发，以响应天线位置优化问题，这对于标准CS而言很难。更重要的是，我们考虑到确保可实现的解决方案的最小元素间间距约束。具体而言，首先提出了离网的正交匹配追踪（OMP）算法，其复杂性低，然后脱离网格前景正交匹配追踪（LAOMP）设计具有更好的合成性能，但复杂性更高。此外，模拟结果表明，与相关方法相比，所提出的方案在计算复杂性和合成性能方面具有更大的优势。
A novel sparse array synthesis method for non-uniform planar arrays is proposed, which belongs to compressive sensing (CS)-based systhesis. Particularly, we propose an off-grid refinement technique to simultaneously optimize the antenna element positions and excitations with a low complexity, in response to the antenna position optimization problem that is difficult for standard CS. More importantly, we take into account the minimum inter-element spacing constraint for ensuring the physically realizable solution. Specifically, the off-grid Orthogonal Match Pursuit (OMP) algorithm is first proposed with low complexity and then off-grid Look Ahead Orthogonal Match Pursuit (LAOMP) is designed with better synthesis performance but higher complexity. In addition, simulation results have shown the proposed schemes have more advantages in computational complexity and synthesis performances compared with the related method.
