College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China.
Sensors (Basel). 2018 Sep 10;18(9):3025. doi: 10.3390/s18093025.
Recently, many sparse-based direction-of-arrival (DOA) estimation methods for coprime arrays have become popular for their excellent detection performance. However, these methods often suffer from grid mismatch problem due to the discretization of the potential angle space, which will cause DOA estimation performance degradation when the target is off-grid. To this end, we proposed a sparse-based off-grid DOA estimation method for coprime arrays in this paper, which includes two parts: coarse estimation process and fine estimation process. In the coarse estimation process, the grid points closest to the true DOAs, named coarse DOAs, are derived by solving an optimization problem, which is constructed according to the statistical property of the vectorized covariance matrix estimation error. Meanwhile, we eliminate the unknown noise variance effectively through a linear transformation. Due to finite snapshots effect, some undesirable correlation terms between signal and noise vectors exist in the sample covariance matrix. In the fine estimation process, we therefore remove the undesirable correlation terms from the sample covariance matrix first, and then utilize a two-step iterative method to update the grid biases. Combining the coarse DOAs with the grid biases, the final DOAs can be obtained. In the end, simulation results verify the effectiveness of the proposed method.
最近，基于稀疏表示的互质阵列方向估计（DOA）方法因其优异的检测性能而受到广泛关注。然而，由于潜在角度空间的离散化，这些方法往往存在栅格失配问题，当目标偏离栅格时，会导致 DOA 估计性能下降。为此，本文提出了一种基于稀疏表示的互质阵列离网 DOA 估计方法，该方法包括两部分：粗估计过程和细估计过程。在粗估计过程中，通过求解根据矢量协方差矩阵估计误差的统计特性构造的优化问题，得到与真实 DOA 最接近的网格点，称为粗 DOA。同时，通过线性变换有效消除未知噪声方差。由于有限快拍效应，样本协方差矩阵中存在信号和噪声向量之间的一些不理想的相关项。因此，在细估计过程中，我们首先从样本协方差矩阵中去除不理想的相关项，然后利用两步迭代方法更新网格偏差。将粗 DOA 与网格偏差相结合，即可得到最终的 DOA。最后，仿真结果验证了所提出方法的有效性。
