存在幅相误差时二维稳健超分辨测角算法

刘敏提; 曾操; 胡树林; 陈建忠; 李军; 李世东; 廖桂生

doi:10.19665/j.issn1001-2400.20231201

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存在幅相误差时二维稳健超分辨测角算法

信息与通信工程 | 更新时间：2024-07-23

- 存在幅相误差时二维稳健超分辨测角算法
- Algorithm for estimation of the two-dimensional robust super-resolution angle under amplitude and phases uncertainty background
- 西安电子科技大学学报 2024年51卷第3期页码：55-62
- 作者机构：
  
  西安电子科技大学雷达信号处理全国重点实验室,陕西西安 710071
- 作者简介：
  
  [ "刘敏提(1993—),女,西安电子科技大学博士研究生,E-mail:[email protected]" ]
  曾操(1979—),男,教授,E-mail:[email protected]
  [ "胡树林(2000—),男,西安电子科技大学硕士研究生,E-mail:[email protected]" ]
  [ "陈建忠(1984—),男,教授,E-mail:[email protected]" ]
  [ "李军(1972—),男,教授,E-mail:[email protected]" ]
  [ "李世东(1968—),男,教授,E-mail:[email protected]" ]
  [ "廖桂生(1963—),男,教授,E-mail:[email protected]" ]
- 基金信息：
  
  国家自然科学基金(61621005);国家自然科学基金(61771015)
- DOI：10.19665/j.issn1001-2400.20231201
  中图分类号： TN911.7
- 纸质出版日期：2024-06-20，
  
  网络出版日期：2023-12-27，
  
  收稿日期：2023-07-01，
- 稿件说明：
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刘敏提, 曾操, 胡树林, 等. 存在幅相误差时二维稳健超分辨测角算法[J]. 西安电子科技大学学报, 2024,51(3):55-62.

Minti LIU, Cao ZENG, Shulin HU, et al. Algorithm for estimation of the two-dimensional robust super-resolution angle under amplitude and phases uncertainty background[J]. Journal of Xidian University, 2024,51(3):55-62.
刘敏提, 曾操, 胡树林, 等. 存在幅相误差时二维稳健超分辨测角算法[J]. 西安电子科技大学学报, 2024,51(3):55-62. DOI： 10.19665/j.issn1001-2400.20231201.

Minti LIU, Cao ZENG, Shulin HU, et al. Algorithm for estimation of the two-dimensional robust super-resolution angle under amplitude and phases uncertainty background[J]. Journal of Xidian University, 2024,51(3):55-62. DOI： 10.19665/j.issn1001-2400.20231201.

摘要

针对4D车载毫米波雷达在俯仰与方位维角度分辨力较低、阵列存在幅相误差时测角有偏的问题

提出一种基于快速稀疏贝叶斯学习的稳健二维超分辨测角方法。首先

利用空域稀疏性特点

对角度域空间进行栅格划分

构建了存在幅相误差时的二维超分辨测角信号模型;然后

通过固定点更新的MacKay SBL重构算法实现了多个邻近目标二维角度估计

并利用基于向量点乘的自校正算法对相位误差进行估计

以对有偏的角度估计进行修正;最后

给出了多输入多输出虚拟阵列下的二维角度估计的克拉美-罗界

并分析了所提算法的计算复杂度。仿真结果表明

在大陆ARS548雷达实际12发16收天线布局下

通过对比6种超分辨测角算法

所提方法在低信噪比、少量快拍下和幅相误差较小时

具有较高的角度分辨力与较低的均方根误差。

Abstract

In order to address the issues of low angle resolution in elevation and azimuth dimensions of the 4D vehicle-mounted millimeter wave radar

as well as the biased angle measurement when the array includes amplitude and phase defects.A robust two-dimensional super-resolution angle estimation method based on fast sparse Bayesian Learning(FSBL) is suggested as a solution to this issue.First

a two-dimensional super-resolution angle signal model with amplitude and phase errors is built by using grids to split the angle domain space depending on spatial sparsity.Then

the two-dimensional angle estimation for spatial proximity targets is obtained using the fixed-point updated based MacKay SBL reconstruction algorithm

with the phase error and biased angle compensation calibrated using the self-correcting algorithm based on vector dot product.Finally

the computational complexity of the proposed algorithm is analyzed

and the Cramer-Rao Lower Bound(CRB) for two-dimensional angle estimation under MIMO non-uniform sparse arrays is provided.By comparing six distinct categories of super-resolution algorithms

simulation results demonstrate that the proposed method has a high angle resolution and a low root mean square error(RMSE) in a low SNR and few snapshot numbers under the actual layout of 12 transmitting and 16 receiving antennas for the continental ARS548 radar.

关键词

超分辨多输入多输出阵列毫米波雷达贝叶斯学习幅相误差

Keywords

super-resolutionmultiple-input multiple-output(MIMO) arraymillimeter wave radarsparse Bayesian learningamplitude and phases error

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