基于几何序列分解与稀疏重构的DOA估计

doi:10.11959/j.issn.1000-436x.2023011

通信学报 ›› 2023, Vol. 44 ›› Issue (1): 153-163.doi: 10.11959/j.issn.1000-436x.2023011

基于几何序列分解与稀疏重构的DOA估计

侯进¹^,², 陈鑫强¹^,²^,³

¹ 西南交通大学信息科学与技术学院智能感知智慧运维实验室，四川成都 611756
² 西南交通大学综合交通大数据应用技术国家工程实验室，四川成都 611756
³ 西南交通大学计算机与人工智能学院，四川成都 611756

修回日期:2022-11-22 出版日期:2023-01-25 发布日期:2023-01-01
作者简介:侯进（1969- ），女，重庆人，博士，西南交通大学副教授，主要研究方向为无线电测向、数字化遗产保护、图像识别等
陈鑫强（1999- ），男，四川眉山人，西南交通大学硕士生，主要研究方向为无线电测向和深度学习
基金资助:
国家重点研发计划基金资助项目(2020YFB1711902)

DOA estimation based on geometric sequence decomposition and sparse reconstruction

Jin HOU¹^,², Xinqiang CHEN¹^,²^,³

¹ IPSOM Lab, School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China
² National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu 611756, China
³ School of Computer and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China

Revised:2022-11-22 Online:2023-01-25 Published:2023-01-01
Supported by:
The National Key Research and Development Program of China(2020YFB1711902)

摘要/Abstract

摘要：

为了解决均匀圆阵在欠定情况下对相干信号测向的问题，提出了一种利用几何序列分解与稀疏重构相结合的波达方向（DOA）估计算法。几何序列分解用于拆分相干组，并估计出每个相干组的实际方向向量，稀疏重构则对每个相干组进行DOA估计。仿真结果表明，当均匀圆阵的阵元数为M时，相比于现有算法，所提算法所能估计的最大信源数为 M(M-1)，并且当信源数较多时，其测向成功率和精度都更优，此外，所提算法能够解决“角度兼并”问题，并且在极少快拍数测向任务中具有一定的优势。

关键词: 几何序列分解, 稀疏重构, 欠定情况, 相干信号, 均匀圆阵

Abstract:

In order to solve the problem of direction finding of coherent signals for uniform circular array under the condition of underdetermination, a direction of arrival (DOA) estimation algorithm combining geometric sequence decomposition and sparse reconstruction was proposed.Geometric sequence decomposition was used to split coherent groups and estimate the actual direction vector of each coherent group, while sparse reconstruction was used to estimate DOA for each coherent group.Simulation results demonstrate that when the number of elements of the uniform circular array is M, compared with the existing algorithms, the maximum number of sources that can be estimated by the proposed algorithm is M(M-1).And when the number of sources is large, the success rate and accuracy of direction finding are better.In addition, the proposed algorithm can solve the “angle merger” problem, and has advantages in the direction finding tasks with very few snapshots.

Key words: geometric sequence decomposition, sparse reconstruction, condition of underdetermination, coherent signal, uniform circular array

中图分类号:

TN98

侯进, 陈鑫强. 基于几何序列分解与稀疏重构的DOA估计[J]. 通信学报, 2023, 44(1): 153-163.

Jin HOU, Xinqiang CHEN. DOA estimation based on geometric sequence decomposition and sparse reconstruction[J]. Journal on Communications, 2023, 44(1): 153-163.

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算法	DOA估计结果	最大误差	错误个数
目标DOA	[[32°,328°],[30°,210°],[89°,272°]]	—	—
OMP算法	[3°,325°,33°,213°,89°,273°]	3°	1
本文所提算法	[[32°,328°],[30°,210°],[89°,272°]]	0°	0

算法	DOA估计结果	最大误差
目标DOA	[[15°],[167°],[251°]]	—
OMP算法	[13°,169°,251°]	2°
MUSIC算法	[14°,164°,251°]	3°
DML算法	[13°,167°,250°]	2°
本文所提算法	[[15°],[167°],[251°]]	0°

基于几何序列分解与稀疏重构的DOA估计

DOA estimation based on geometric sequence decomposition and sparse reconstruction

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