基于稀疏贝叶斯学习的快速DOA估计算法

郑阳; 司伟建; 刘睿智; 曲明超; 薛雅琳

doi:10.12263/DZXB.20260642

您当前的位置：

首页 >

文章列表页 >

基于稀疏贝叶斯学习的快速DOA估计算法

更新时间：2026-10-13

- 基于稀疏贝叶斯学习的快速DOA估计算法
- Fast DOA Estimation Algorithm Based on Sparse Bayesian Learning
- 电子学报 2026年页码：1-13
- 作者机构：
  
  1.哈尔滨工程大学信息与通信工程学院，黑龙江哈尔滨 150001
  2.鹏城实验室，广东深圳 518108
- 作者简介：
  
  郑阳男，2003年1月出生于河南省周口市。现为哈尔滨工程大学博士研究生。主要研究方向为阵列信号处理、稀疏贝叶斯学习DOA估计等。E-mail: zy_heu127@163.com
  司伟建男，1971年5月出生于黑龙江省哈尔滨市。现为哈尔滨工程大学研究员。主要研究方向为阵列信号处理、宽带信号检测、信号分选、数字信道化等。E-mail: swj0418@263.net
  刘睿智男，1989年1月出生于黑龙江省哈尔滨市。现为哈尔滨工程大学预聘教授。主要研究方向为天线与超表面、无源测向定位。E-mail: ruizhi.liu@hrbeu.edu.cn
  曲明超男，1998年3月出生于河南省南阳市。现为鹏城实验室博士后。主要研究方向为阵列信号处理、极化敏感阵列DOA估计、信号基础大模型等。E-mail: qu_ming_chao@163.com
  薛雅琳女，2002年1月出生于河南省郑州市。现为哈尔滨工程大学博士研究生。主要研究方向为阵列信号处理、DOA估计解相干算法研究等。E-mail: xyl995111@163.com
- 基金信息：
  
  国家自然科学基金(U24B6013)
- DOI：10.12263/DZXB.20260642
  中图分类号： TN953;
- 收稿：2026-08-18，
  
  录用：2026-08-30，
  
  网络首发：2026-10-13，
- 稿件说明：
移动端阅览
郑阳, 司伟建, 刘睿智, 等. 基于稀疏贝叶斯学习的快速DOA估计算法[J/OL]. 电子学报, 2026,1-13.

ZHENG Yang, SI Weijian, LIU Ruizhi, et al. Fast DOA Estimation Algorithm Based on Sparse Bayesian Learning[J/OL]. ACTA ELECTRONICA SINICA, 2026, 1-13.
郑阳, 司伟建, 刘睿智, 等. 基于稀疏贝叶斯学习的快速DOA估计算法[J/OL]. 电子学报, 2026,1-13. DOI： 10.12263/DZXB.20260642.

ZHENG Yang, SI Weijian, LIU Ruizhi, et al. Fast DOA Estimation Algorithm Based on Sparse Bayesian Learning[J/OL]. ACTA ELECTRONICA SINICA, 2026, 1-13. DOI： 10.12263/DZXB.20260642.

摘要

针对波达方向（Direction of Arrival，DOA）估计中稀疏贝叶斯学习类算法实时性差的问题，本文提出一种基于稀疏贝叶斯学习的快速DOA估计算法。该算法首先采用一种噪声方差预估计方法，在贝叶斯学习迭代前对噪声方差进行独立预估计，从而有效减少贝叶斯学习迭代中未知参数的数量，加快稀疏贝叶斯学习的收敛速度；其次，将空域滤波器和观测协方差矩阵重构结合，通过构造与待估计信号方向匹配的空域滤波器，抑制非目标方向上的能量干扰。同时，针对真实入射信号能量在完备空间网格点上呈现的簇状分布特性，对观测协方差矩阵进行局部重构，提取与目标方向相关联的一簇网格点所对应的协方差信息，减弱信号能量在相邻网格间的泄漏对完备空间功率谱值的影响，从而提高DOA估计精度；最后，为了解决信号频率捷变等问题，所提算法不依赖阵列平移不变性，能够适配非均匀阵列结构，实现对多频段信号进行DOA估计，增强了算法在不同阵列配置和频率条件下的适用性。计算机仿真实验表明，在低信噪比和少快拍数下，所提算法在保证高精度估计的同时，运行时间较对比算法降低了约37.27%，并适用于0.8~6 GHz多频段范围内的窄带信号。最后在微波暗室中用无人机（Unmanned Aerial Vehicle，UAV）图传信号和标准信号源进行了实测实验，进一步验证了所提算法对无人机图传信号和标准信号源信号等混合信号具有快速收敛和稳定的DOA估计性能。

Abstract

To address the poor real-time performance of sparse Bayesian learning-based algorithms in direction of arrival (DOA) estimation

this paper proposes a fast DOA estimation algorithm based on sparse Bayesian learning. First

the proposed algorithm adopts a noise variance pre-estimation method to independently estimate the noise variance prior to the Bayesian learning iterations

thereby effectively reducing the number of unknown parameters in the Bayesian learning iterations and accelerating the convergence of sparse Bayesian learning. Second

the proposed algorithm combines spatial filtering with reconstruction of the observation covariance matrix. By constructing a spatial filter matched to the directions of the signals to be estimated

energy interference from non-target directions is suppressed. Meanwhile

in view of the cluster distribution characteristics of the true incident signal energy over the complete spatial grid

a local reconstruction of the observation covariance matrix is performed to extract the covariance information corresponding to a cluster of grid points associated with the target directions

thereby mitigating the influence of signal energy leakage between adjacent grid points on the complete spatial power spectrum and improving the DOA estimation accuracy. Finally

to tackle issues such as frequency agility of signals

the proposed algorithm does not rely on array translational invariance

is applicable to non-uniform arrays and enables DOA estimation for multi-band signals

thereby enhancing its applicability under different array configurations and frequency conditions. Computer simulation results show that under low signal-to-noise ratios and a small number of snapshots

the proposed algorithm ensures high estimation accuracy while reducing the runtime by 37.27% compared with benchmark algorithms

and is applicable to narrowband signals within the multi-band range of 0.8 GHz to 6 GHz. In addition

experiments were conducted in a microwave anechoic chamber using unmanned aerial vehicle (UAV) video transmission signals and standard signal sources

further verifying that the proposed algorithm achieves fast convergence and stable DOA estimation performance for mixed signals comprising UAV video transmission signals and standard signal sources.

关键词

Keywords

references

Wen Fangqing , Zhang Zhe , Sun Hang , et al . 2D-DOA estimation auxiliary localization of anonymous UAV using EMVS-MIMO radar [J ] . IEEE Internet of Things Journal , 2024 , 11 ( 9 ): 16255 - 16266 . DOI: 10.1109/jiot.2024.3351136 http://dx.doi.org/10.1109/jiot.2024.3351136

Li Yi , Xia Weijie , Zhu Lingzhi , et al . High resolution DOA estimation of coherent distributed millimeter-wave radar [J ] . IEEE Transactions on Aerospace and Electronic Systems , 2025 , 61 ( 3 ): 6098 - 6109 . DOI: 10.1109/taes.2024.3525444 http://dx.doi.org/10.1109/taes.2024.3525444

Pucci L , Paolini E , Giorgetti A . System-level analysis of joint sensing and communication based on 5G new radio [J ] . IEEE Journal on Selected Areas in Communications , 2022 , 40 ( 7 ): 2043 - 2055 . DOI: 10.1109/jsac.2022.3155522 http://dx.doi.org/10.1109/jsac.2022.3155522

Guo Yuehao , Wang Xianpeng , Lan Xiang , et al . Traffic target location estimation based on tensor decomposition in intelligent transportation system [J ] . IEEE Transactions on Intelligent Transportation Systems , 2024 , 25 ( 1 ): 816 - 828 . DOI: 10.1109/tits.2022.3165584 http://dx.doi.org/10.1109/tits.2022.3165584

Schmidt R . Multiple emitter location and signal parameter estimation [J ] . IEEE Transactions on Antennas and Propagation , 1986 , 34 ( 3 ): 276 - 280 . DOI: 10.1109/tap.1986.1143830 http://dx.doi.org/10.1109/tap.1986.1143830

Roy R , Kailath T . ESPRIT-estimation of signal parameters via rotational invariance techniques [J ] . IEEE Transactions on Acoustics, Speech, and Signal Processing , 1989 , 37 ( 7 ): 984 - 995 . DOI: 10.1109/29.32276 http://dx.doi.org/10.1109/29.32276

Xu Kaijie , Xing Mengdao , Zhang Rui , et al . High-accuracy DOA estimation algorithm at low SNR through exploiting a supervised index [J ] . IEEE Transactions on Aerospace and Electronic Systems , 2022 , 58 ( 4 ): 3658 - 3665 . DOI: 10.1109/taes.2022.3144121 http://dx.doi.org/10.1109/taes.2022.3144121

Tian Quan , Cai Ruiyan . A low-complexity DOA estimation algorithm for distributed source localization [J ] . IEEE Transactions on Instrumentation and Measurement , 2023 , 72 : 8000504 . DOI: 10.1109/tim.2023.3234034 http://dx.doi.org/10.1109/tim.2023.3234034

Malioutov D , Cetin M , Willsky A S . A sparse signal reconstruction perspective for source localization with sensor arrays [J ] . IEEE Transactions on Signal Processing , 2005 , 53 ( 8 ): 3010 - 3022 . DOI: 10.1109/tsp.2005.850882 http://dx.doi.org/10.1109/tsp.2005.850882

Ji Shihao , Xue Ya , Carin L . Bayesian compressive sensing [J ] . IEEE Transactions on Signal Processing , 2008 , 56 ( 6 ): 2346 - 2356 . DOI: 10.1109/tsp.2007.914345 http://dx.doi.org/10.1109/tsp.2007.914345

徐明 , 唐倩 . 基于原子范数的波达方向与时延联合估计方法 [J ] . 通信学报 , 2024 , 45 ( 10 ): 153 - 162 . DOI: 10.11959/j.issn.1000-436x.2024186 http://dx.doi.org/10.11959/j.issn.1000-436x.2024186

Xu Ming , Tang Qian . Joint DOA-delay estimation approach based on atomic norm [J ] . Journal on Communications , 2024 , 45 ( 10 ): 153 - 162 . (in Chinese) . DOI: 10.11959/j.issn.1000-436x.2024186 http://dx.doi.org/10.11959/j.issn.1000-436x.2024186

Lv Liujie , Wu Sheng , Su Yi , et al . Sparse Bayesian learning unfolding network for efficient DoA estimation in low SNR [J ] . IEEE Transactions on Vehicular Technology , 2025 , 74 ( 9 ): 13985 - 13996 . DOI: 10.1109/tvt.2025.3560445 http://dx.doi.org/10.1109/tvt.2025.3560445

Liu Zhangmeng , Huang Zhitao , Zhou Yiyu . An efficient maximum likelihood method for direction-of-arrival estimation via sparse Bayesian learning [J ] . IEEE Transactions on Wireless Communications , 2012 , 11 ( 10 ): 1 - 11 . DOI: 10.1109/twc.2012.090312.111912 http://dx.doi.org/10.1109/twc.2012.090312.111912

Dai Jisheng , Bao Xu , Xu Weichao , et al . Root sparse Bayesian learning for off-grid DOA estimation [J ] . IEEE Signal Processing Letters , 2017 , 24 ( 1 ): 46 - 50 . DOI: 10.1109/lsp.2016.2636319 http://dx.doi.org/10.1109/lsp.2016.2636319

王琦森 , 余华 , 李杰 , 等 . 基于稀疏贝叶斯学习的空间紧邻信号DOA估计算法 [J ] . 电子与信息学报 , 2021 , 43 ( 3 ): 708 - 716 .

Wang Qisen , Yu Hua , Li Jie , et al . Sparse Bayesian learning based algorithm for DOA estimation of closely spaced signals [J ] . Journal of Electronics & Information Technology , 2021 , 43 ( 3 ): 708 - 716 . (in Chinese)

Zheng Chundi , Yu Meiyi , Shan Jiaolong , et al . Fast sparse non-negative least squares via ADMM for high resolution DOA estimation [J ] . IEEE Sensors Journal , 2023 , 23 ( 4 ): 3901 - 3910 . DOI: 10.1109/jsen.2022.3233820 http://dx.doi.org/10.1109/jsen.2022.3233820

Fu Haosheng , Dai Fengzhou , Hong Ling . Off-grid error calibration for DOA estimation based on sparse Bayesian learning [J ] . IEEE Transactions on Vehicular Technology , 2023 , 72 ( 12 ): 16293 - 16307 . DOI: 10.1109/tvt.2023.3298965 http://dx.doi.org/10.1109/tvt.2023.3298965

Pan Xiang , Wang Haoran , Li Min , et al . Fast estimation of array shape and direction of arrival using sparse Bayesian learning for manoeuvring towed line array [J ] . IET Radar , Sonar & Navigation, 2024 , 18 ( 10 ): 1625 - 1637 . DOI: 10.1049/rsn2.12598 http://dx.doi.org/10.1049/rsn2.12598

Zhao Yang , Wu Jianxin , Fang Yuyuan , et al . Countering cross-eye jamming signal with fast sparse Bayesian learning for subarray MIMO radar [J ] . IEEE Transactions on Vehicular Technology , 2025 , 74 ( 9 ): 13728 - 13743 . DOI: 10.1109/tvt.2025.3559526 http://dx.doi.org/10.1109/tvt.2025.3559526

Krayani A , Alam A S , Marcenaro L , et al . A novel resource allocation for anti-jamming in cognitive-UAVs: An active inference approach [J ] . IEEE Communications Letters , 2022 , 26 ( 10 ): 2272 - 2276 . DOI: 10.1109/lcomm.2022.3190971 http://dx.doi.org/10.1109/lcomm.2022.3190971

Qu Lin , Xu Guanjun , Zeng Zhi , et al . UAV-assisted RF/FSO relay system for space-air-ground integrated network: A performance analysis [J ] . IEEE Transactions on Wireless Communications , 2022 , 21 ( 8 ): 6211 - 6225 . DOI: 10.1109/twc.2022.3147823 http://dx.doi.org/10.1109/twc.2022.3147823

王钦源 , 柴蓉 , 孙瑞锦 , 等 . 面向用户随机特性的联合无人机部署与资源分配算法 [J ] . 电子学报 , 2024 , 52 ( 12 ): 4015 - 4022 .

Wang Qinyuan , Chai Rong , Sun Ruijin , et al . Random user characteristics-oriented joint UAV deployment and resource allocation algorithm [J ] . Acta Electronica Sinica , 2024 , 52 ( 12 ): 4015 - 4022 . (in Chinese)

Jin Yi , He Di , Wei Shuang , et al . Off-grid DOA estimation method based on sparse Bayesian learning with clustered structural-aware prior information [J ] . IEEE Transactions on Vehicular Technology , 2024 , 73 ( 4 ): 5469 - 5483 . DOI: 10.1109/tvt.2023.3335959 http://dx.doi.org/10.1109/tvt.2023.3335959

Qu Mingchao , Si Weijian , Liu Ruizhi . Array design and phase interferometer-based DOA estimation for diversely polarized antenna arrays [J ] . Measurement , 2025 , 242 : 116222 . DOI: 10.1016/j.measurement.2024.116222 http://dx.doi.org/10.1016/j.measurement.2024.116222

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于稀疏贝叶斯学习的OTFS系统脉冲噪声抑制方法

基于稀疏贝叶斯学习的GFDM系统联合迭代信道估计与符号检测

基于中心频率-调频率域重构的多普勒空变目标高分辨SA-ISAR成像与横向定标

一种有效的压缩感知图像重建算法

低轨卫星网络自适应随机接入研究