Root sparse asymptotic minimum variance for off-grid direction-of-arrival estimation
Root sparse asymptotic minimum variance for off-grid direction-of-arrival estimation
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离网到达方向估计的根稀疏渐近最小方差
DOI：
10.1016/j.sigpro.2019.05.024
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发表时间：
2019-10
影响因子：
4.4
通讯作者：
Guo Xijing
中科院分区：
文献类型：
--
作者：
Zhang Yahao;Yang Yixin;Yang Long;Guo Xijing
基于稀疏性的波达方向（DOA）估计算法因其良好的性能而受到广泛关注。然而，这些算法中的大多数遭受所谓的基础失配，如果真正的波达方向偏离离散网格点。本文提出了一种迭代更新网格直到部分网格点与DOA重合的离网格DOA估计方法。基于真实导向矢量的一阶泰勒级数展开，这些网格点与真实DOA的偏差，即，所谓的网格误差通过近似线性关系与在网格上定义的导向矢量相关联。利用这种线性关系，可以在渐进最小方差准则下估计网格误差。通过将网格误差添加到网格点来修改网格，使它们更接近真实DOA，从而减轻基失配。仿真结果表明，该方法在估计精度和计算效率方面都具有较高的性能。
Sparsity-based direction-of-arrival (DOA) estimation algorithms have received much attention due to their good performances. However, most of these algorithms suffer from the so-called basis mismatch if the true DOAs deviate from the discrete grid points. This paper provides an off-grid DOA estimation method which iteratively updates the grid until some of the grid points coincide with the DOAs. Based on the first-order Taylor series expansion of the true steering vectors, the deviations of these grid points from the true DOAs, i.e., the so-called grid errors, are linked to the steering vectors defined on the grid by a linear relation approximately. Using this linear relation, the grid errors can be estimated under the asymptotic minimum variance criterion. The grid is modified by adding the grid errors to the grid points, leading to them closer to the true DOAs, and consequently mitigating the basis mismatch. It is shown by simulations that the proposed method achieves high performance both in terms of estimation accuracy and computational efficiency.
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两种基于稀疏的离网到达方向估计方法
影响因子：
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