| 压缩对称嵌套阵列和稀疏信号重构的近场目标方位估计 |
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| 引用本文： | 李双,郑大青,刘伟,胡顺仁,何为. 压缩对称嵌套阵列和稀疏信号重构的近场目标方位估计[J]. 声学技术, 2018, 37(1): 82-88 |
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| 作者姓名： | 李双 郑大青 刘伟 胡顺仁 何为 |
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| 作者单位： | 重庆理工大学电气与电子工程学院;中国科学院上海微系统与信息技术研究所无线传感网与通信重点实验室; |
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| 基金项目： | 重庆市基础与前沿研究计划项目（cstc2015jcyjA040055）；重庆市教委科学技术研究项目（KJ1500917，KJ1600936） |
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| 摘 要： | 针对现有近场源估计算法中近场源数量受限于阵元数的问题,提出了一种基于稀疏对称嵌套阵列和稀疏信号重构的近场欠定波达方向估计方法。首先利用四阶累积量,将二维空间参数估计问题转化为一维参数估计问题,同时得到差分阵列;为了进一步提高估计分辨率与减少估计误差,对虚拟阵列的接收信号在空间域进行稀疏表示;最后通过L1范数最小二乘法得到目标源的波达方向。相较于现有算法,该方法可以估计更多的目标源,并且有更低的均方误差与更高的分辨率。实验仿真验证了算法的有效性与优越性。
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| 关 键 词： | 阵列信号处理 欠定波达方向估计 近场 稀疏信号重构 四阶累积量 |
| 收稿时间： | 2017-03-17 |
| 修稿时间： | 2017-05-03 |
Direction-of-arrival estimation of near-field sources based on compressed symmetric nested array and sparse signal reconstruction |
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| LI Shuang,ZHENG Da-qing,LIU Wei,HU Shun-ren and HE Wei. Direction-of-arrival estimation of near-field sources based on compressed symmetric nested array and sparse signal reconstruction[J]. Technical Acoustics, 2018, 37(1): 82-88 |
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| Authors: | LI Shuang ZHENG Da-qing LIU Wei HU Shun-ren HE Wei |
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| Affiliation: | School of Electrical and Electronic Engineering, Chongqing University of Technology, Chongqing 400054, China,School of Electrical and Electronic Engineering, Chongqing University of Technology, Chongqing 400054, China,School of Electrical and Electronic Engineering, Chongqing University of Technology, Chongqing 400054, China,School of Electrical and Electronic Engineering, Chongqing University of Technology, Chongqing 400054, China and Key Labarotory of Wireless Sensor Networks and Communication, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200180, China |
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| Abstract: | In this paper, a novel underdetermined direction-of-arrival (DOA) estimation method based on compressed symmetric nested array and sparse signal representation is proposed in the near-field. Firstly, the forth order cumulants are employed to transform the original two-dimensional parameter estimation problem into a one-dimensional one and to obtain the difference co-array of the physical array. Then, in order to further increase the angular resolution and reduce the estimate error, the received signals of the virtual array are sparsely represented in spatial domain. Finally, the DOAs of the sources are founded through the use of the L1-regularized least square method. Compared to the existing methods, the proposed approach can process more sources and have lower variance and higher resolution. Simulation results are given to demonstrate the effectiveness and efficiency of the proposed method. |
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| Keywords: | array signal processing underdetermined direction-of-arrival estimation near-field sparse signal recovery fourth order cumulant |
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