存在幅相误差下的稳健稀疏贝叶斯二维波达方向估计

王绪虎; 金序; 侯玉君; 张群飞; 徐振华; 王辛杰; 陈建军

doi:10.12382/bgxb.2023.0778

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存在幅相误差下的稳健稀疏贝叶斯二维波达方向估计

更新时间：2025-08-18

- 存在幅相误差下的稳健稀疏贝叶斯二维波达方向估计
- Robust Sparse Bayesian Two-dimensional DOA Estimation with Gain-phase Errors
- 兵工学报 2024年45卷第10期页码：3608-3618
- 作者机构：
  
  1. 青岛理工大学信息与控制工程学院, 山东青岛 266520
  2. 中国科学院海洋研究所海洋环流与波动重点实验室, 山东青岛 266071
  3. 西北工业大学航海学院, 陕西西安 710072
- 作者简介：
  
  *邮箱: wangxuhu@qut.edu.cn
- 基金信息：
  
  国家自然科学基金项目(62171247);山东省自然科学基金项目(ZR2021QF113);山东省自然科学基金项目(ZR2022MF273)
- DOI：10.12382/bgxb.2023.0778
  中图分类号：
- 收稿：2023-08-22，
  
  网络出版：2024-10-30，
  
  纸质出版：2024-10-31
- 稿件说明：
移动端阅览
王绪虎, 金序, 侯玉君, 等. 存在幅相误差下的稳健稀疏贝叶斯二维波达方向估计[J]. 兵工学报, 2024,45(10):3608-3618.

Xuhu WANG, Xu JIN, Yujun HOU, et al. Robust Sparse Bayesian Two-dimensional DOA Estimation with Gain-phase Errors[J]. Acta Armamentarii, 2024, 45(10): 3608-3618.
王绪虎, 金序, 侯玉君, 等. 存在幅相误差下的稳健稀疏贝叶斯二维波达方向估计[J]. 兵工学报, 2024,45(10):3608-3618. DOI： 10.12382/bgxb.2023.0778.

Xuhu WANG, Xu JIN, Yujun HOU, et al. Robust Sparse Bayesian Two-dimensional DOA Estimation with Gain-phase Errors[J]. Acta Armamentarii, 2024, 45(10): 3608-3618. DOI： 10.12382/bgxb.2023.0778.

摘要

为减小传感器幅相误差的影响

提升方位估计性能

针对L型传感器阵列提出一种存在幅相误差下的稳健稀疏贝叶斯二维波达方向(Direction-Of-Arrival

DOA)估计方法。引入一个辅助角

将二维DOA估计问题转化为两个一维角度估计问题。利用L型阵列两子阵数据互协方差矩阵的对角线元素向量

构造一个含有幅相误差的稀疏表示模型

采用期望最大算法推导未知参数表达式并进行迭代运算

进而获得离网格和信号精度

利用二者构建新的空间谱函数

通过谱峰搜索估计出辅助角;将求得辅助角代入含有幅相误差的阵列接收数据稀疏表示模型

再次运用稀疏贝叶斯学习方法

估计出入射信号的俯仰角;根据3个角之间的关系

估计出方位角。研究结果表明:该方法实现了方位角和俯仰角的自动匹配

进一步克服了幅相误差对估计性能的影响

提高了方位估计的精度和角度分辨力

尤其是在高信噪比和幅相误差较大情况下优势更明显;仿真结果验证了该方法的有效性。

Abstract

To reduce the influence of gain-phase errors and improve the performance of direction-of-arrival (DOA) estimation

a robust sparse Bayesian two-dimensional DOA estimation method with gain-phase errors is proposed for the L-shaped sensor array. In the proposed method

an auxiliary angle is introduced to transform a 2D DOA estimation problem into two 1D angle estimation problems. A sparse representation model with gain-phase errors is constructed by using the diagonal element vector of the cross-covariance matrix of two submatrices of L-shaped sensor array. The expectation maximization algorithm is used to derive the unknown parameter expression

which is used to perform the iterative operations for obtaining the off-grid and the precision of signal. A new spatial spectral function is constructed by using the off-grid and the precision of signal. The auxiliary angle can be estimated by searching the new spatial spectra peak. The estimated auxiliary angle is introduced into the sparse representation model of the received data with gain-phase errors

and then the sparse Bayesian learning method is used to estimate the elevation angle of incident signal. According to the relationship among three angles

the azimuth angle can be estimated. The results show that this method realizes the automatic matching of azimuth angle and elevation angle

and improves the accuracy of DOA estimation and angle resolution. Simulated results verify the effectiveness of the proposed method.

关键词

Keywords

references

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