Volume 14 Issue 2
Apr.  2025
Turn off MathJax
Article Contents
Abstract
References
Article Navigation >  Journal of Radars  > 2025  >  14(2): 269-279
Xiong Dingding, Cui Guolong, Kong Lingjiang, Yang Xiaobo. Micro-motion Parameter Estimation in Non-Gaussian Noise via Mutual Correntropy[J]. Journal of Radars, 2017, 6(3): 300-308. doi: 10.12000/JR17007
Citation: XIANG Houhong, WANG Yongliang, LI Yuxi, et al. Multipath suppression and high-precision angle measurement method based on feature game preprocessing[J]. Journal of Radars, 2025, 14(2): 269–279. doi: 10.12000/JR24215
shu

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

DOI: 10.12000/JR24215 CSTR: 32380.14.JR24215
  • 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
  • Received Date: 2024-10-27
  • Rev Recd Date: 2024-12-31
    • Available Online: 2025-01-04
  • Publish Date: 2025-01-20
    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 (DoA). 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.

     

    • FullText(HTML)
    • References(21)
  • [1]
    吴剑旗. 先进米波雷达[M]. 北京: 国防工业出版社, 2015: 294.

    WU Jianqi. Advanced Metric Wave Radar[M]. Beijing: National Defense Industry Press, 2015: 294.
    [2]
    WU Jianqi, ZHU Wei, and CHEN Baixiao. Compressed sensing techniques for altitude estimation in multipath conditions[J]. IEEE Transactions on Aerospace and Electronic Systems, 2015, 51(3): 1891–1900. doi: 10.1109/TAES.2015.130841.
    [3]
    王鸿帧, 郑桂妹, 陈晨, 等. 米波雷达低仰角估计技术分析与展望[J]. 火力与指挥控制, 2023, 48(11): 6–16. doi: 10.3969/j.issn.1002-0640.2023.11.002.

    WANG Hongzhen, ZHENG Guimei, CHEN Chen, et al. Analysis and prospect of low elevation estimation technology for meter wave radar[J]. Fire Control & Command Control, 2023, 48(11): 6–16. doi: 10.3969/j.issn.1002-0640.2023.11.002.
    [4]
    郑轶松, 陈伯孝. 米波雷达低仰角目标多径模型及其反演方法研究[J]. 电子与信息学报, 2016, 38(6): 1468–1474. doi: 10.11999/JEIT151013.

    ZHENG Yisong and CHEN Baixiao. Multipath model and inversion method for low-angle target in very high frequency radar[J]. Journal of Electronics & Information Technology, 2016, 38(6): 1468–1474. doi: 10.11999/JEIT151013.
    [5]
    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.
    [6]
    ROY R and 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.
    [7]
    ZISKIND I and WAX M. Maximum likelihood localization of diversely polarized sources by simulated annealing[J]. IEEE Transactions on Antennas and Propagation, 1990, 38(7): 1111–1114. doi: 10.1109/8.55625.
    [8]
    LE JEUNE D, JARRY P, SALAUN A, et al. Performance evaluation of Marple modified FBLP and MINNORM methods for angle of arrival estimation using real multipath active sonar data[C]. The Fifth ASSP Workshop on Spectrum Estimation and Modeling, Rochester, USA, 1990: 352–356. doi: 10.1109/SPECT.1990.205606.
    [9]
    PAN Jingjing, SUN Meng, WANG Yide, et al. Simplified spatial smoothing for DOA estimation of coherent signals[J]. IEEE Transactions on Circuits and Systems II: Express Briefs, 2023, 70(2): 841–845. doi: 10.1109/TCSII.2022.3214321.
    [10]
    ZISKIND I and WAX M. Maximum likelihood localization of multiple sources by alternating projection[J]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1988, 36(10): 1553–1560. doi: 10.1109/29.7543.
    [11]
    项厚宏. 基于深度学习的米波雷达阵列超分辨DOA估计方法研究[D]. [博士论文], 西安电子科技大学, 2021. doi: 10.27389/d.cnki.gxadu.2021.000059.

    XIANG Houhong. Study on super-resolution DOA estimation methods based on deep learning for VHF radar[D]. [Ph.D. dissertation], Xidian University, 2021. doi: 10.27389/d.cnki.gxadu.2021.000059.
    [12]
    李存勖. 米波雷达低仰角测高相关问题研究[D]. [博士论文], 西安电子科技大学, 2018.

    LI Cunxu. Study on some issues of altitude measurement of low-angle target for VHF array radar[D]. [Ph.D. dissertation], Xidian University, 2018.
    [13]
    朱伟. 米波数字阵列雷达低仰角测高方法研究[D]. [博士论文], 西安电子科技大学, 2013.

    ZHU Wei. Study on low-angle altitude measurement in VHF radar[D]. [Ph.D. dissertation], Xidian University, 2013.
    [14]
    李妙歌, 陈渤, 王东升, 等. 面向SAR图像目标分类的CNN模型可视化方法[J]. 雷达学报(中英文), 2024, 13(2): 359–373. doi: 10.12000/JR23107.

    LI Miaoge, CHEN Bo, WANG Dongsheng, et al. CNN model visualization method for SAR image target classification[J]. Journal of Radars, 2024, 13(2): 359–373. doi: 10.12000/JR23107.
    [15]
    张群, 胡健, 罗迎, 等. 微动目标雷达特征提取、成像与识别研究进展[J]. 雷达学报, 2018, 7(5): 531–547. doi: 10.12000/JR18049.

    ZHANG Qun, HU Jian, LUO Ying, et al. Research progresses in radar feature extraction, imaging, and recognition of target with micro-motions[J]. Journal of Radars, 2018, 7(5): 531–547. doi: 10.12000/JR18049.
    [16]
    牟效乾, 陈小龙, 关键, 等. 基于INet的雷达图像杂波抑制和目标检测方法[J]. 雷达学报, 2020, 9(4): 640–653. doi: 10.12000/JR20090.

    MOU Xiaoqian, CHEN Xiaolong, GUAN Jian, et al. Clutter suppression and marine target detection for radar images based on INet[J]. Journal of Radars, 2020, 9(4): 640–653. doi: 10.12000/JR20090.
    [17]
    XU Xiaoxuan and HUANG Qinghua. MD-DOA: A model-based deep learning DOA estimation architecture[J]. IEEE Sensors Journal, 2024, 24(12): 20240–20253. doi: 10.1109/JSEN.2024.3396337.
    [18]
    LABBAF N, DALILI OSKOUEI H R, and ABEDI M R. Robust DOA estimation in a uniform circular array antenna with errors and unknown parameters using deep learning[J]. IEEE Transactions on Green Communications and Networking, 2023, 7(4): 2143–2152. doi: 10.1109/TGCN.2023.3294448.
    [19]
    YU Jiarun and WANG Yafeng. Deep learning-based multipath DoAs estimation method for mmWave massive MIMO systems in low SNR[J]. IEEE Transactions on Vehicular Technology, 2023, 72(6): 7480–7490. doi: 10.1109/TVT.2023.3239402.
    [20]
    LIU Zhangmeng, ZHANG Chenwei, and YU P S. Direction-of-arrival estimation based on deep neural networks with robustness to array imperfections[J]. IEEE Transactions on Antennas and Propagation, 2018, 66(12): 7315–7327. doi: 10.1109/TAP.2018.2874430.
    [21]
    WU Liuli and HUANG Zhitao. Coherent SVR learning for wideband direction-of-arrival estimation[J]. IEEE Signal Processing Letters, 2019, 26(4): 642–646. doi: 10.1109/LSP.2019.2901641.
    • Relative Articles

  • Relative Articles

    [1]WANG Xiang, WANG Yumiao, CHEN Xingyu, ZANG Chuanfei, CUI Guolong. Deep Learning-based Marine Target Detection Method with Multiple Feature Fusion[J]. Journal of Radars, 2024, 13(3): 554-564. doi: 10.12000/JR23105
    [2]ZHANG Yushi, LI Xiaoyu, ZHANG Jinpeng, XIA Xiaoyun. Sea Clutter Spectral Parameters Prediction and Influence Factor Analysis Based on Deep Learning[J]. Journal of Radars, 2023, 12(1): 110-119. doi: 10.12000/JR22133
    [3]LI Yi, DU Lan, DU Yuang. Convolutional Neural Network Based on Feature Decomposition for Target Detection in SAR Images[J]. Journal of Radars, 2023, 12(5): 1069-1080. doi: 10.12000/JR23004
    [4]DONG Yunlong, ZHANG Zhaoxiang, DING Hao, HUANG Yong, LIU Ningbo. Target Detection in Sea Clutter Using a Three-feature Prediction-based Method[J]. Journal of Radars, 2023, 12(4): 762-775. doi: 10.12000/JR23037
    [5]XU Shuwen, BAI Xiaohui, GUO Zixun, SHUI Penglang. Status and Prospects of Feature-based Detection Methods for Floating Targets on the Sea Surface (in English)[J]. Journal of Radars, 2020, 9(4): 684-714. doi: 10.12000/JR20084
    [6]LIU Ningbo, DONG Yunlong, WANG Guoqing, DING Hao, HUANG Yong, GUAN Jian, CHEN Xiaolong, HE You. Sea-detecting X-band Radar and Data Acquisition Program (in English)[J]. Journal of Radars, 2019, 8(5): 656-667. doi: 10.12000/JR19089
    [7]ZHANG Lamei, ZHANG Siyu, DONG Hongwei, ZHU Sha. Robust Classification of PolSAR Images Based on Pinball loss Support Vector Machine[J]. Journal of Radars, 2019, 8(4): 448-457. doi: 10.12000/JR19055
    [8]ZHANG Xinxun, ZHOU Shenghua, LIU Hongwei. Influence of Target Polarization Scattering Characteristics on the Detection Performance of Polarization Diversity Radar[J]. Journal of Radars, 2019, 8(4): 510-518. doi: 10.12000/JR18112
    [9]DING Hao, LIU Ningbo, DONG Yunlong, CHEN Xiaolong, GUAN Jian. Overview and Prospects of Radar Sea Clutter Measurement Experiments[J]. Journal of Radars, 2019, 8(3): 281-302. doi: 10.12000/JR19006
    [10]CHEN Shichao, LUO Feng, HU Chong, NIE Xueya. Small Target Detection in Sea Clutter Background Based on Tsallis Entropy of Doppler Spectrum[J]. Journal of Radars, 2019, 8(3): 344-354. doi: 10.12000/JR19012
    [11]ZUO Lei, CHAN Xiuxiu, LU Xiaofei, LI Ming. A Weak Target Detection Method in Sea Clutter Based on Joint Space-time-frequency Decomposition[J]. Journal of Radars, 2019, 8(3): 335-343. doi: 10.12000/JR19035
    [12]Tao Chensong, Chen Siwei, Li Yongzhen, Xiao Shunping. Polarimetric SAR Terrain Classification Using Polarimetric Features Derived from Rotation Domain[J]. Journal of Radars, 2017, 6(5): 524-532. doi: 10.12000/JR16131
    [13]Zeng Lina, Zhou Deyun, Li Xiaoyang, Zhang Kun. Novel SAR Target Detection Algorithm Using Free Training[J]. Journal of Radars, 2017, 6(2): 177-185. doi: 10.12000/JR16114
    [14]Shao Luyi, Hong Wen. Decision Tree Classification of PolSAR Image Based on Two-dimensional Polarimetric Features[J]. Journal of Radars, 2016, 5(6): 681-691. doi: 10.12000/JR16002
    [15]Zhang Zenghui, Yu Wenxian. Feature Understanding and Target Detection for Sparse Microwave Synthetic Aperture Radar Images[J]. Journal of Radars, 2016, 5(1): 42-56. doi: 10.12000/JR15097
    [16]Ding Hao, Dong Yunlong, Liu Ningbo, Wang Guoqing, Guan Jian. Overview and Prospects of Research on Sea Clutter Property Cognition[J]. Journal of Radars, 2016, 5(5): 499-516. doi: 10.12000/JR16069
    [17]Chen Xiao-long, Dong Yun-long, Li Xiu-you, Guan Jian. Modeling of Micromotion and Analysis of Properties of Rigid Marine Targets[J]. Journal of Radars, 2015, 4(6): 630-638. doi: 10.12000/JR15079
    [18]Zhao Yong-ke, Lü Xiao-De. A Joint-optimized Real-time Target Detection Algorithm for Passive Radar[J]. Journal of Radars, 2014, 3(6): 666-674. doi: 10.12000/JR14005
    [19]Yan Liang, Sun Pei-lin, Yi Lei, Han Ning, Tang Jun. Modeling of Compound Gaussian Sea Clutter Based on Inverse Gaussian Distribution[J]. Journal of Radars, 2013, 2(4): 461-465. doi: 10.3724/SP.J.1300.2013.13083
    [20]Chen Xiao-lng, Guan jian, He You. Applications and Prospect of Micro-motion Theory in the Detection of Sea Surface Target[J]. Journal of Radars, 2013, 2(1): 123-134. doi: 10.3724/SP.J.1300.2012.20102
    • Supplements(0)
    • Cited By

  • Cited by

    Periodical cited type(3)

    1. 宋永坤，晏天兴，张可，刘显，戴永鹏，金添. 基于点云时空特征的超宽带雷达轻量化人体行为识别方法. 雷达学报(中英文). 2025(01): 1-15 .
    2. 张锐，龚汉钦，宋瑞源，李亚东，卢智，张东恒，胡洋，陈彦. 基于4D成像雷达的隔墙人体姿态重建与行为识别研究. 雷达学报(中英文). 2025(01): 44-61 .
    3. 陈彦，张锐，李亚东，宋瑞源，耿瑞旭，龚汉钦，汪斌全，张东恒，胡洋. 基于无线信号的人体姿态估计综述. 雷达学报(中英文). 2025(01): 229-247 .

    Other cited types(3)

  • Created with Highcharts 5.0.7Amount of accessChart context menuAbstract Views, HTML Views, PDF Downloads StatisticsAbstract ViewsHTML ViewsPDF Downloads2024-052024-062024-072024-082024-092024-102024-112024-122025-012025-022025-032025-04020406080100
    Created with Highcharts 5.0.7Chart context menuAccess Class DistributionFULLTEXT: 21.4 %FULLTEXT: 21.4 %META: 68.4 %META: 68.4 %PDF: 10.2 %PDF: 10.2 %FULLTEXTMETAPDF
    Created with Highcharts 5.0.7Chart context menuAccess Area Distribution其他: 15.0 %其他: 15.0 %其他: 0.7 %其他: 0.7 %China: 1.0 %China: 1.0 %Yeonsu-gu: 0.3 %Yeonsu-gu: 0.3 %上海: 3.1 %上海: 3.1 %东莞: 0.7 %东莞: 0.7 %乌鲁木齐: 0.3 %乌鲁木齐: 0.3 %保定: 0.3 %保定: 0.3 %保德曼: 0.3 %保德曼: 0.3 %信阳: 0.3 %信阳: 0.3 %列克星敦: 1.0 %列克星敦: 1.0 %北京: 7.8 %北京: 7.8 %十堰: 0.3 %十堰: 0.3 %华盛顿州: 0.3 %华盛顿州: 0.3 %南京: 2.0 %南京: 2.0 %南昌: 0.7 %南昌: 0.7 %合肥: 1.4 %合肥: 1.4 %咸阳: 1.0 %咸阳: 1.0 %嘉兴: 0.3 %嘉兴: 0.3 %天津: 0.7 %天津: 0.7 %安康: 0.3 %安康: 0.3 %宣城: 0.7 %宣城: 0.7 %巴中: 0.3 %巴中: 0.3 %常州: 0.3 %常州: 0.3 %平顶山: 0.3 %平顶山: 0.3 %广州: 3.1 %广州: 3.1 %开封: 0.7 %开封: 0.7 %弗吉: 0.3 %弗吉: 0.3 %张家口: 8.8 %张家口: 8.8 %张家界: 0.7 %张家界: 0.7 %惠州: 1.7 %惠州: 1.7 %成都: 0.3 %成都: 0.3 %扬州: 0.7 %扬州: 0.7 %无锡: 0.3 %无锡: 0.3 %昆明: 3.4 %昆明: 3.4 %朝阳: 0.3 %朝阳: 0.3 %杭州: 2.7 %杭州: 2.7 %桂林: 0.7 %桂林: 0.7 %武汉: 0.7 %武汉: 0.7 %沈阳: 1.0 %沈阳: 1.0 %淄博: 0.3 %淄博: 0.3 %温州: 0.3 %温州: 0.3 %湖州: 0.3 %湖州: 0.3 %湘潭: 0.7 %湘潭: 0.7 %漯河: 3.1 %漯河: 3.1 %石家庄: 0.3 %石家庄: 0.3 %秦皇岛: 0.3 %秦皇岛: 0.3 %绍兴: 0.3 %绍兴: 0.3 %芒廷维尤: 21.1 %芒廷维尤: 21.1 %芝加哥: 1.4 %芝加哥: 1.4 %衡水: 0.3 %衡水: 0.3 %西宁: 1.7 %西宁: 1.7 %西安: 0.3 %西安: 0.3 %诺沃克: 0.7 %诺沃克: 0.7 %迈阿密: 0.3 %迈阿密: 0.3 %郑州: 0.3 %郑州: 0.3 %鄂州: 0.7 %鄂州: 0.7 %重庆: 0.3 %重庆: 0.3 %长沙: 1.0 %长沙: 1.0 %青岛: 0.3 %青岛: 0.3 %齐齐哈尔: 0.3 %齐齐哈尔: 0.3 %其他其他ChinaYeonsu-gu上海东莞乌鲁木齐保定保德曼信阳列克星敦北京十堰华盛顿州南京南昌合肥咸阳嘉兴天津安康宣城巴中常州平顶山广州开封弗吉张家口张家界惠州成都扬州无锡昆明朝阳杭州桂林武汉沈阳淄博温州湖州湘潭漯河石家庄秦皇岛绍兴芒廷维尤芝加哥衡水西宁西安诺沃克迈阿密郑州鄂州重庆长沙青岛齐齐哈尔

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Get Citation
    PDF
    XML
    Article views(201) PDF downloads(30) Cited by(6)
    Proportional views
    Related

    京ICP备20021838号-8   Supported by: Beijing Renhe Information Technology Co. Ltd     百度统计

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return