高分辨波达方位估计技术研究进展

赵唯; 李璇; 郝程鹏

doi:10.11993/j.issn.2096-3920.2023-0158

高分辨波达方位估计技术研究进展

doi: 10.11993/j.issn.2096-3920.2023-0158

赵唯^{1, 2,},
李璇^{1, 2,},
郝程鹏^{1, 2,}

1.
中国科学院声学研究所, 北京, 100190
2.
中国科学院大学电子电气与通信工程学院, 北京, 100049

基金项目: 中国科学院声学研究所自主部署“前沿探索”类项目资助(QYTS202013).

详细信息

作者简介:
赵唯：赵　唯(1998-), 女, 在读博士, 主要研究方向为波达方位估计及目标跟踪

中图分类号: TJ630; U674
计量
- 文章访问数: 43
- HTML全文浏览量: 19
- PDF下载量: 20
- 被引次数: 0
出版历程
- 收稿日期: 2023-12-09
- 修回日期: 2024-04-07
- 录用日期: 2024-04-07
- 网络出版日期: 2024-11-26

Research Progress in Methods to Estimate High-resolution Direction of Arrival

ZHAO Wei^{1, 2
,},
LI Xuan^{1, 2
,},
HAO Chengpeng^{1, 2
,}

1.
Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China
2.
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China

摘要

摘要: 随着阵列信号处理的广泛应用, 波达方位(DOA)估计作为阵列信号处理的核心问题得到较大发展。文中首先对基于均匀线性阵列的窄带目标方位估计中的基于波束形成的传统算法和近10年的新兴算法进行了总结。分析了传统波束形成方法分辨力受限的原因, 讨论了自适应波束形成方位谱、子空间方法以及压缩感知等更高分辨力的方法; 进一步, 基于实际应用的需要, 总结了宽带目标方位估计方法、基于稀疏阵列的DOA估计方法以及二维DOA估计方法的进展。最后介绍了基于人工智能的方法在DOA方位估计中的新进展。文中的研究可应用于现代雷达声呐探测、无线电通信以及导航中, 具有较高的应用价值。
- 信号处理 /
- 波达方位 /
- 高分辨 /
- 宽带 /
- 稀疏阵列
Abstract: With the widespread application of array signal processing, the estimation of direction of arrival(DOA) as the core problem of array signal processing has made significant progress. This paper first summarizes the traditional algorithms based on beamforming for narrowband target direction estimation relying on uniform linear arrays and emerging algorithms in the past decade. Then, it analyzes the reasons for the limited resolution of traditional beamforming-based methods and discusses higher-resolution methods such as adaptive beamforming direction spectrum, subspace methods, and compressed sensing. Furthermore, for the needs of practical applications, the paper summarizes the progress of broadband target DOA estimation methods, sparse array-based DOA estimation methods, and two-dimensional DOA estimation methods. Finally, the new advances of artificial intelligence-based methods in DOA estimation are introduced. The research in this paper can be applied to modern radar/sonar detection, radio communication, and navigation, showing high application value.
- signal processing /
- direction of arrival /
- high resolution /
- broadband /
- sparse array

HTML全文

图 1 窄带目标方位估计方法发展过程

注: MUSIC为多重信号分类(multiple signal classification); ESPRIT为旋转矢量不变技术(estimating signal parameter via rotational invariance techniques); OGSBL为基于离网模型的稀疏贝叶斯学习(off-grid sparse Bayesian learning); SPA为稀疏参数估计算法(sparse paramaters algorithm)

Figure 1. Development process of narrowband target DOA estimation methods

下载: 全尺寸图片幻灯片

表 1 窄带目标DOA估计方法优缺点对比

Table 1. Comparison of advantages and disadvantages of narrowband target DOA estimation methods

方法	算法	优点	缺点
基于波束形成的算法	常规波束形成	稳健性强; 计算复杂度低	分辨率低
基于波束形成的算法	自适应波束形成	旁瓣低; 分辨率高	计算复杂度高
子空间算法	MUSIC类算法	高信噪比、多块拍下分辨率高	低信噪比、小块拍下分辨率低; 难以处理相干信号
子空间算法	ESPRIT类算法	高信噪比、多块拍下分辨率高	低信噪比、小块拍下分辨率低; 难以处理相干信号; 要求阵列平移不变
基于压缩感知的算法	凸松弛优化算法	稳健性强; 分辨率高; 可处理相干信号	计算复杂度高; 需要参数选择
	贪婪算法	计算复杂度低; 可处理相干信号	分辨率低
	SBL算法	稳健性强; 无需稀疏度先验信息; 可处理相干信号	计算复杂度高

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