论文标题
通过关节稀疏和低级别信号恢复的非连接子阵列的到达估计方向
Direction of Arrival Estimation for Non-Coherent Sub-Arrays via Joint Sparse and Low-Rank Signal Recovery
论文作者
论文摘要
从相干天线阵列获得的单个快照中估算多个来源的到达方向（DOA）是一个众所周知的问题，可以通过稀疏信号重建方法来解决，其中DOA是从恢复后的高维信号的峰值中估算出来的。在本文中，我们考虑了一个更具挑战性的DOA估计任务，其中该阵列由非固定子阵列组成（即，由于使用低成本非同步的本地振荡器而观察到不同的未知相移的子阵列）。我们将此问题提出，因为重建了关节稀疏和低级别基质并解决了其凸松弛。虽然可以从凸问题的解决方案中估算DOA，但我们进一步展示了如果相反地从此解决方案估算相位移动，创建“相位校正”的观察结果并应用了另一个基于最终的（朴素，连贯）基于稀疏的DOA的估计，则如何获得改进。数值实验表明，所提出的方法的表现优于基于子阵列以及其他基于稀疏的方法的非连通处理的策略。
Estimating the directions of arrival (DOAs) of multiple sources from a single snapshot obtained by a coherent antenna array is a well-known problem, which can be addressed by sparse signal reconstruction methods, where the DOAs are estimated from the peaks of the recovered high-dimensional signal. In this paper, we consider a more challenging DOA estimation task where the array is composed of non-coherent sub-arrays (i.e., sub-arrays that observe different unknown phase shifts due to using low-cost unsynchronized local oscillators). We formulate this problem as the reconstruction of a joint sparse and low-rank matrix and solve its convex relaxation. While the DOAs can be estimated from the solution of the convex problem, we further show how an improvement is obtained if instead one estimates from this solution the phase shifts, creates "phase-corrected" observations and applies another final (plain, coherent) sparsity-based DOA estimation. Numerical experiments show that the proposed approach outperforms strategies that are based on non-coherent processing of the sub-arrays as well as other sparsity-based methods.
