| 三重混合范数的L型嵌套阵列二维角度估计 |
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| 引用本文： | 陈璐,毕大平,潘继飞.三重混合范数的L型嵌套阵列二维角度估计[J].宇航学报,2019,40(1):94-101. |
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| 作者姓名： | 陈璐 毕大平 潘继飞 |
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| 作者单位： | 1.国防科技大学电子对抗学院,合肥 230037；
2. 安徽省电子制约技术重点实验室，合肥 230037 |
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| 基金项目： | 国家自然科学基金（61671453）；安徽省自然科学基金（1608085MF123） |
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| 摘 要： | 针对L型嵌套阵列二维角度估计问题，提出一种三重混合范数块稀疏重构算法。首先，建立一种俯仰角和方位角可分离的二维稀疏测向模型，将两个维度采样点分块，分别计算联合协方差矩阵，二维角度估计问题被转化为联合协方差矩阵稀疏优化问题；为减小计算复杂度，建立三重混合范数块稀疏重构模型，利用交叉迭代的方法得到稀疏解，实现了二维角度估计，并且可以自动配对。仿真试验表明，三重混合范数稀疏重构算法能够有效估计多个辐射源的二维角度，分辨率较高，并且具有一定的鲁棒性。与传统算法相比，在低信噪比和小快拍数的条件下，均优于传统二维角度估计算法。
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| 关 键 词： | 嵌套阵列 压缩感知 二维角度估计 混合范数 块稀疏 |
| 收稿时间： | 2018-03-09 |
Two Dimension Angle Estimation of L Shaped Nested Array Based on Triple Mixed Norms |
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| CHEN Lu,BI Da ping,PAN Ji fei.Two Dimension Angle Estimation of L Shaped Nested Array Based on Triple Mixed Norms[J].Journal of Astronautics,2019,40(1):94-101. |
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| Authors: | CHEN Lu BI Da ping PAN Ji fei |
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| Affiliation: | 1.College of Electronic Countermeasures, National University of Defense Technology, Hefei 230037, China; 2.Key Laboratory of Electronic Restriction, Hefei 230037, China |
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| Abstract: | A block sparse reconstruction algorithm based on the triple mixed norms is proposed to solve the problem of L-shaped nested array two-dimension angle estimation. Firstly, a two-dimension sparse direction finding model is established. In this model, the azimuth and elevation can be separated. The sampling points of the two dimensions are divided into blocks. The joint covariance matrixes are calculated separately. The two-dimension angle estimation problem is transformed into a joint covariance matrix sparse optimization problem. In order to reduce the computational complexity, a triple mixed norms block sparse reconstruction model is established. The sparse solution is achieved by the cross iteration method. The 2-D angle estimation is realized, and the angles of the two dimensions can be automatically matched. The simulation results show that the triple mixed norms sparse reconstruction algorithm can effectively estimate the two-dimension angle of the multiple sources, with high resolution and robustness. Compared with the traditional algorithm, at low SNR conditions and a small number of snapshots, the algorithm is superior to the traditional 2D angle estimation. |
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| Keywords: | Nested array Compressed sensing Two-dimension angle estimation Mixed norm Block sparsity |
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