基于特征博弈预处理的多径抑制与高精度测角方法

项厚宏; 王永良; 李雨曦; 陈毓锋; 王凤玉; 曾小路

doi:10.12000/JR24215

基于特征博弈预处理的多径抑制与高精度测角方法

DOI: 10.12000/JR24215

1.
合肥工业大学计算机与信息学院合肥 230009
2.
空军预警学院武汉 430019
3.
西安电子科技大学杭州研究院杭州 311200
4.
北京邮电大学人工智能学院北京 100876
5.
北京理工大学信息与电子学院北京 100081

基金项目: 国家自然科学基金(62201189)，安徽省重大基础研究项目(2023z04020018)，西安电子科技大学杭州研究院院士工作站基金(XH-KY-202306-0285)，中央高校基本科研业务费专项资金(JZ2024HGTB0228)

详细信息

作者简介:
项厚宏，博士，讲师，硕士生导师，主要研究方向为智能雷达信号处理、阵列信号处理等

王永良，博士，教授，博士生导师，主要研究方向为雷达信号处理、空时信号处理、阵列信号处理等

李雨曦，本科生，主要研究方向为智能参数估计等

陈毓锋，博士，副研究员，主要研究方向为雷达通信一体化、阵列信号处理等

王凤玉，博士，讲师，主要研究方向为智能信号处理、阵列信号处理等

曾小路，博士，副研究员，主要研究方向为穿墙雷达静止目标成像、智能无线感知与物联网技术

通讯作者:
项厚宏 hhxiang@hfut.edu.cn

王永良 ylwangkjld@163.com

责任主编：陈伯孝 Corresponding Editor: CHEN Baixiao
中图分类号: TN958
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出版历程
- 收稿日期: 2024-10-27
- 修回日期: 2024-12-31

Multipath Suppression and High-precision Angle Measurement Method Based on Feature Game Preprocessing

1.
School of Computer and Information, Hefei University of Technology, Hefei 230009, China
2.
Air Force Early Warning Academy, Wuhan 430019, China
3.
Hangzhou Institute of Technology, Xidian University, Hangzhou 311200, China
4.
School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China
5.
School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China

Funds: The National Natural Science Foundation of China (62201189), Key Fundamental Research Program of Anhui Province (2023z04020018), The Open Fund for the Hangzhou Institute of Technology Academician Workstation at Xidian University (XH-KY-202306-0285), The Fundamental Research Funds for the Central University (JZ2024HGTB0228)

More Information

Corresponding author: XIANG Houhong, hhxiang@hfut.edu.cn; WANG Yongliang, ylwangkjld@163.com

摘要

摘要: 米波雷达波束较宽，探测低仰角目标时多径信号严重影响直达信号的显著性，低仰角测角性能较差。针对此问题，该文提出了一种信号级特征博弈的多径抑制与高精度测角方法，构建一组直达信号提取器和直达信号特征检验器，直达信号提取器挖掘出多径信号湮没的直达信号，直达信号特征检验器用于鉴别、分析提取的直达信号的有效性，直达信号提取器和直达信号特征检验器相互博弈、优化，有效实现直达信号增强和多径信号抑制的效果，并利用已有的超分辨算法进行波达方向估计(DOA)。计算机仿真结果表明，所提算法不依赖于严格的目标角度信息，能够有效抑制多径信号，经典的超分辨算法在多种场景下的估计性能显著提升，且较已有的有监督学习模型而言，所提算法对未知的信号参数及多径分布模型具有更好的泛化性。
- 米波雷达 /
- 多径信号 /
- 直达信号提取 /
- 直达信号特征鉴别 /
- 角度测量
Abstract: The meter-wave radar, known for its wide beamwidth, often faces challenges in detecting low-elevation targets due to interference from multipath signals. These reflected signals diminish the strength of the direct signal, leading to poor accuracy in low-elevation angle measurements. To solve this problem, this paper proposes a multipath suppression and high-precision angle measurement method. This method, based on a signal-level feature game approach, incorporates two interconnected components working together. The direct signal extractor mines the direct signal submerged within the multipath signal. The direct signal feature discriminator ensures the integrity and validity of the extracted direct signal. By continuously interacting and optimizing one another, these components suppress the multipath interference effectively and enhance the quality of the direct signal. The refined signal is then processed using advanced super-resolution algorithms to estimate the direction of arrival. Computer simulations have shown that the proposed algorithm achieves high performance without relying on strict target angle information, effectively suppressing multipath signals. This approach noticeably enhances the estimation accuracy of classic super-resolution algorithms. Compared to existing supervised learning models, the proposed algorithm offers better generalization to unknown signal parameters and multipath distribution models.
- Meter-wave radar /
- Multipath signal /
- Direct signal extraction /
- Direct signal feature identification /
- Angle measurement

HTML全文

图 1 多径信号模型

Figure 1. Multipath signal model

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图 2 特征博弈预处理的多径抑制方法框图

Figure 2. A block diagram of multipath suppression method using feature game preprocessing

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图 3 特征博弈预处理前后数据相位分布分析

Figure 3. Phase distribution analysis of data before and after feature game preprocessing

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图 4 特征博弈预处理前后数据空间谱分析

Figure 4. Spatial spectrum analysis of data before and after feature game preprocessing

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图 5 不同信噪比条件下测角误差分布和均方根误差对比

Figure 5. Error distribution of DOA estimation and root mean square error comparison under different SNR conditions

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图 6 测角均方根误差与快拍数关系曲线

Figure 6. The relationship curve between root mean square error of direction of arrival estimation and number of snapshots

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图 7 特征博弈预处理方法泛化性分析

Figure 7. Generalization analysis of feature game preprocessing method

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图 8 模型失配条件下的测角性能分析

Figure 8. Analysis of DOA estimation performance under model mismatch conditions

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