A High-Accuracy Direction of Arrival Estimation Algorithm for Impact Noise Resistance

SHAN Zebiao; YAO Ruiguang; LIU Xiaosong; XUE Hongyao; LIU Yunqing

doi:10.7652/xjtuxb202504018

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A High-Accuracy Direction of Arrival Estimation Algorithm for Impact Noise Resistance

更新时间：2026-01-06

- A High-Accuracy Direction of Arrival Estimation Algorithm for Impact Noise Resistance
- Journal of Xi’an Jiaotong University Vol. 59, Issue 4, Pages: 193-202(2025)
- 作者机构：
  
  1.长春理工大学电子信息工程学院， 130022，长春
  2.吉林大学通信工程学院， 130022，长春
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202504018
  CLC： TN911
- Received：07 November 2024，
  
  Online First：31 December 2024，
  
  Published：10 April 2025
- 稿件说明：
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SHAN Zebiao, YAO Ruiguang, LIU Xiaosong, et al. A High-Accuracy Direction of Arrival Estimation Algorithm for Impact Noise Resistance[J]. Journal of Xi’an Jiaotong University, 2025, 59(4): 193-202.
DOI：

SHAN Zebiao, YAO Ruiguang, LIU Xiaosong, et al. A High-Accuracy Direction of Arrival Estimation Algorithm for Impact Noise Resistance[J]. Journal of Xi’an Jiaotong University, 2025, 59(4): 193-202. DOI： 10.7652/xjtuxb202504018.

摘要

针对现有波达方向（DOA）估计算法在冲击噪声下估计精度较低的问题，提出了一种基于相控分数低阶矩和指数族分布函数的离格近似

范数DOA估计算法。首先，利用相控分数低阶矩抑制冲击噪声，构建稀疏DOA模型；其次，通过对平滑函数的平滑性和陡峭性进行研究，构造了一类平滑性和陡峭性可变的函数族，并利用该函数族求解初始支撑集；然后，提出了一种前向展望和后向延拓策略，对初始支撑集进行扩展，并从中选择出可以使残差最小化的一组支撑集为最优支撑集，从而确定在格DOA估计；最后，为了解决网格失配效应，利用DOA粗估计与离格偏差之间的联合稀疏性，通过交替方向迭代联合求解DOA粗估计与离格偏差，获得离格DOA估计值。仿真结果表明：与正交匹配追踪算法相比，在信噪比为－8 dB时，所提算法的DOA估计精度提高了66.36%。此外，当冲击噪声特征指数大于0.6时，所提算法对目标DOA的估计误差可以控制在1°以内。仿真结果充分说明了所提算法可在冲击噪声下实现高精度的DOA估计。

Abstract

To address the issue of low estimation accuracy in existing direction of arrival (DOA) estimation algorithms under impulsive noise

an off-grid approximation

-norm DOA estimation algorithm based on phased fractional low-order moments (PFLOM) and exponential family distribution functions is proposed. Firstly

PFLOM is employed to suppress impulsive noise and construct a sparse DOA model. Secondly

by analyzing the smoothness

and steepness of smoothing functions

a family of functions with variable smoothness and steepness is constructed. This function family is used to solve the initial support set. Then

a forward-looking and backward-extending strategy is introduced to expand the initial support set

selecting a group of support sets that minimizes the residual to determine the optimal support set for grid-based DOA estimation. Finally

to address grid mismatch effects

the joint sparsity between DOA coarse estimates and off-grid deviation is used to jointly solve DOA coarse estimates and off-grid deviations through alternating direction iterative methods to obtain off-grid DOA estimate. Simulation results show that

compared to the orthogonal matching pursuit algorithm

the proposed algorithm improves the DOA estimation accuracy by 66.36% at a signal-to-noise ratio of-8dB. Furthermore

when the impulsive noise characteristic index exceeds 0.6

the estimation error of the target DOA by the proposed algorithm can be controlled within 1°. These results demonstrate that the proposed algorithm can achieve high-precision DOA estimation under impulsive noise.

关键词

Keywords

references

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