基于噪声子空间矢量的OMP离格DOA估计

赵洋; 石屹然; 石要武

doi:10.37188/OPE.20202810.2384

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基于噪声子空间矢量的OMP离格DOA估计

信息科学 | 更新时间：2020-11-16

- 基于噪声子空间矢量的OMP离格DOA估计
- DOA Estimation based on OMP modified by noise subspace vectors
- 光学精密工程 2020年28卷第10期页码：2384-2391
- 作者机构：
  
  吉林大学通信工程学院, 吉林长春 130022
- 作者简介：
  
  [ "赵洋(1986-), 男, 吉林长春人, 博士研究生, 2009年于吉林大学获得学士学位, 2012年于吉林大学硕转博直接攻读博士学位, 主要从事阵列信号处理, 信号检测及压缩感知理论研究。E-mail:zhaoyang_yes@163.com" ]
  石屹然, E-mail:shiyiran@jlu.edu.cnSHI Yi-ran, E-mail: shiyiran@jlu.edu.cn
- 基金信息：
  
  国家自然科学基金资助项目(61571462)
- DOI：10.37188/OPE.20202810.2384
  中图分类号： TN911.7
- 收稿日期：2020-06-20，
  
  修回日期：2020-08-18，
  
  录用日期：2020-8-18，
  
  纸质出版日期：2020-10-25
- 稿件说明：
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赵洋, 石屹然, 石要武. 基于噪声子空间矢量的OMP离格DOA估计[J]. 光学精密工程, 2020,28(10):2384-2391.

Yang ZHAO, Yi-ran SHI, Yao-wu SHI. DOA Estimation based on OMP modified by noise subspace vectors[J]. Optics and precision engineering, 2020, 28(10): 2384-2391.
赵洋, 石屹然, 石要武. 基于噪声子空间矢量的OMP离格DOA估计[J]. 光学精密工程, 2020,28(10):2384-2391. DOI： 10.37188/OPE.20202810.2384.

Yang ZHAO, Yi-ran SHI, Yao-wu SHI. DOA Estimation based on OMP modified by noise subspace vectors[J]. Optics and precision engineering, 2020, 28(10): 2384-2391. DOI： 10.37188/OPE.20202810.2384.

摘要

稀疏恢复算法进行DOA估计时需要在角度空间进行网格化量化处理。针对该量化过程会引入的量化误差从而影响估计性能的问题，本文通过导向矢量的一阶泰勒展开式将量化误差引入阵列输出的二阶矩模型。基于该模型设计了一种使用噪声子空间矢量进行修正的OMP算法对DOA和量化误差进行联合估计。新算法基于阵列协方差矩阵对于快拍数的依赖稍显敏感但是弥补了贪婪算法对DOA分辨力的不足并且不需要预知信源个数，同时计算量对比现有的基于

范数约束的凸优化方法大大降低。仿真实验验证了所提算法的有效性。

Abstract

The sparse recovery algorithm needs to perform grid quantization processing in the angle space when DOA estimation is performed. Aiming at the problem that the quantization error introduced by the quantization process affects the estimation performance

this paper introduced the quantization error into the second-order moment model of the array output through the first-order Taylor expansion of the steering vector. Based on this model

an OMP algorithm that used noise subspace vectors to modify was designed to jointly estimate DOA and quantization error. The new algorithm based on the array covariance matrix was slightly sensitive to the dependency on the number of snapshots

but it made up for the lack of DOA resolution of the greedy algorithm and did not require the number of sources to be predicted. At the same time

the amount of calculation was compared with the existing convex based on the Lp norm constraint. The optimization method was greatly reduced. Simulation experiments verify the effectiveness of the proposed algorithm.

关键词

Keywords

references

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