A Spatial Filtering Based Gridless DOA Estimation Method for Coherent Sources
A Spatial Filtering Based Gridless DOA Estimation Method for Coherent Sources
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DOI：
10.1109/access.2018.2872578
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发表时间：
2018-09
期刊：
影响因子：
3.9
通讯作者：
Xiaohuan Wu;Weiping Zhu;Jun Yan;Zeyun Zhang
中科院分区：
文献类型：
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作者：
Xiaohuan Wu;Weiping Zhu;Jun Yan;Zeyun Zhang
本文研究了相关/相干信源环境下的一种协方差矩阵重构方法（CMRA）。我们将空间滤波（SF）模型到我们最近开发的方法CMRA，以提高其适应能力。特别地，提出了一种滑动窗口方案来估计源的数量，并提供了一个迭代过程来估计信号的DOA。由于原始CMRA提供了不准确的估计噪声功率，这是不可取的迭代过程中，提出了一种新的更新规则的噪声功率。此外，我们推导出一个快速实现的SF-CMRA加速DOA估计在每次迭代。该方法适用于均匀线阵和稀疏线阵，能够提供DOA、信号功率和噪声功率的精确估计。我们还表明，所提出的算法框架可以很容易地扩展到其他无网格DOA估计方法的精度提高。仿真结果表明了所提方法的优越性。
In this paper, we investigate a covariance matrix reconstruction approach (CMRA) for direction-of-arrival (DOA) estimation in correlated/coherent sources scenario. We incorporate a spatial filtering (SF) model into our recently developed method CMRA in order to enhance its adaptation ability. In particular, a sliding window scheme is proposed to estimate the number of sources, and an iterative procedure is provided to estimate the DOAs of the signals. Since the original CMRA provides inaccurate estimate of the noise power which is undesirable during iterations, a new update rule for the noise power is proposed. Moreover, we derive a fast implementation of the SF-CMRA to accelerate the DOA estimation in each iteration. The proposed methods are suitable for both uniform and sparse linear arrays and are able to provide accurate estimates of DOAs, signal powers, and noise power. We also show that the proposed algorithmic framework can be easily extended to other gridless DOA estimation methods for accuracy improvement. Simulation results are provided to illustrate the superiority of our proposed methods.