Direction-of-arrival estimation based on Toeplitz covariance matrix reconstruction
Direction-of-arrival estimation based on Toeplitz covariance matrix reconstruction
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DOI：
10.1109/icassp.2016.7472242
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发表时间：
2016-03
期刊：
影响因子：
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通讯作者：
Xiaohuan Wu;Weiping Zhu;Jun Yan
中科院分区：
文献类型：
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作者：
Xiaohuan Wu;Weiping Zhu;Jun Yan
本文解决了波达方向（DOA）估计问题，目的是消除基于稀疏性的方法的离网效应，并扩大基于子空间的方法中可区分信号的最大数量。我们首先重建 Toeplitz 结构中阵列输出的协方差矩阵，然后使用重建的协方差矩阵与 root-MUSIC 一起估计 DOA。所提出的协方差矩阵重建方法（CMRA）可用于均匀和稀疏线性阵列。它还可以利用阵列几何形状来估计大于传感器数量的多个信号的 DOA。与基于稀疏性的方法相比，CMRA 是在连续角度空间而不是离散角度空间中制定的，因此它不受离网效应的影响。进行模拟以验证我们方法的有效性。
This paper addresses the issue of direction-of-arrival (DOA) estimation with an objective to eliminate the off-grid effect of the sparsity-based methods and enlarge the maximum number of distinguishable signals in the subspace-based methods. We first reconstruct the covariance matrix of the array output in the Toeplitz structure and then employ the reconstructed covariance matrix together with root-MUSIC to estimate the DOAs. The proposed covariance matrix reconstruction approach (CMRA) can be used for uniform and sparse linear arrays. It can also estimate the DOAs of multiple signals that are larger than the number of sensors by taking advantage of the array geometry. In contrast to the sparsity-based methods, CMRA is formulated in the continuous angle space rather than the discretized one, and hence it is immune to the off-grid effect. Simulations are carried out to verify the effectiveness of our method.