Two sparse-based methods for off-grid direction-of-arrival estimation
Two sparse-based methods for off-grid direction-of-arrival estimation
复制标题
两种基于稀疏的离网到达方向估计方法
DOI：
10.1016/j.sigpro.2017.07.004
复制
发表时间：
2018
影响因子：
4.4
通讯作者：
Zhang Zeyun
中科院分区：
文献类型：
--
作者：
Wu Xiaohuan;Zhu Wei-Ping;Yan Jun;Zhang Zeyun
最近，人们提出了许多基于稀疏的方法用于波达方向（DOA）估计。然而，这些方法经常遇到由势角空间离散化引起的网格失配问题。他们中的大多数采用迭代网格细化（IGR）方法来缓解这个问题。然而，IGR需要高计算量，并且可能不符合压缩感知（CS）理论中的受限等距属性（RIP）条件。本文旨在克服传统稀疏技术固有的网格失配限制。特别是，我们首先通过将偏差参数合并到信号模型中来引入离网模型，然后提出一种名为离网ℓ1Cholesky协方差分解（OGL1CCD）的两步迭代方法来解决DOA估计问题。我们的方法可以加速以节省计算量，并且所提出的算法框架可以扩展到任何其他基于稀疏的方法，以提高其估计精度。然后，我们提出了另一种基于协方差矩阵模型的离网方法，称为离网ℓ1协方差矩阵重建方法（OGL1CMRA）。与 OGL1CCD 相比，OGL1CMRA 计算效率更高且更准确，但需要足够的快照和不相关的源。我们提出的方法在估计性能方面优于许多其他方法，这已通过大量数值模拟得到验证。
Recently, many sparse-based methods have been proposed for direction-of-arrival (DOA) estimation. However, these methods often suffer from the grid mismatch problem caused by the discretization of the potential angle space. Most of them employ the iterative grid refinement (IGR) method to alleviate this problem. Nevertheless, IGR requires a high computational load and may not comply with the restricted isometry property (RIP) condition in the compressed sensing (CS) theory. This paper aims to overcome the grid mismatch limitation inherent in conventional sparse-based techniques. In particular, we first introduce an off-grid model by incorporating the bias parameter into the signal model, then propose a two-step iterative method named off-grid ℓ1Cholesky covariance decomposition (OGL1CCD) to solve the DOA estimation problem. Our method can be accelerated to save computations and the proposed algorithm framework can be extended for any other sparse-based method to improve their estimation accuracy. We then propose another off-grid method named off-grid ℓ1covariance matrix reconstruction approach (OGL1CMRA) based on the covariance matrix model. Compared to OGL1CCD, OGL1CMRA is more computationally efficient and accurate, but requires sufficient snapshots and uncorrelated sources. Our proposed methods are superior to many other methods in estimation performance, which is verified by extensive numerical simulations.
登录
查看更多内容
影响因子：
4.4
作者：
FRIEDLANDER, B
通讯作者：
FRIEDLANDER, B
影响因子：
5.4
作者：
Malioutov, D;Çetin, M;Willsky, AS
通讯作者：
Willsky, AS
影响因子：
2.9
作者：
Ottersten, B;Stoica, P;Roy, R
通讯作者：
Roy, R
影响因子：
5.4
作者：
Yang, Zai;Xie, Lihua;Zhang, Cishen
通讯作者：
Zhang, Cishen
DOI：
10.1109/twc.2012.090312.111912
发表时间：
2012-10-01
影响因子：
10.4
作者：
Liu, Zhang-Meng;Huang, Zhi-Tao;Zhou, Yi-Yu
通讯作者：
Zhou, Yi-Yu