
浏览全部资源
扫码关注微信
东南大学 信息科学与工程学院 毫米波国家重点实验室, 江苏 南京 210096
Received:09 October 2021,
Published Online:10 March 2023,
Published:28 February 2023
移动端阅览
Peng CHEN, Zhen XU, Zhenxin CAO, et al. Graph Feature Learning-Based Sea Clutter Suppression Method[J]. Acta Armamentarii, 2023, 44(2): 534-544.
Peng CHEN, Zhen XU, Zhenxin CAO, et al. Graph Feature Learning-Based Sea Clutter Suppression Method[J]. Acta Armamentarii, 2023, 44(2): 534-544. DOI: 10.12382/bgxb.2021.0662.
为有效降低海杂波对海洋雷达的工作影响
研究海杂波的抑制问题
提出一种基于图特征学习的海杂波抑制算法。使用时频变换对雷达回波信号进行维度扩增
基于图嵌入处理深度挖掘图结构特征的思想
并依据海杂波和目标回波信号在时频谱中的不同结构特性
给出一种通过图嵌入进行信号节点特征向量构造的方法。区别于传统时域对消和子空间分解等方法
该方法可以通过时频谱中不同信号的节点分类实现海杂波的抑制。仿真与实测结果表明
该算法可以有效抑制雷达回波信号中的海杂波分量
提升雷达回波信号的信杂比
为海洋雷达进行海杂波的抑制提供了新的思路和途径。
In order to reduce the effect of sea clutter on Marine radar
a sea clutter suppression algorithm based on graph feature learning is proposed. The time-frequency transform is used to amplify the dimension of radar echo signal
and based on the idea that the graph structure features can be deeply mined by graph embedding processing
a method of constructing signal node feature vector by graph embedding is presented according to the different structural characteristics of sea clutter and target echo signal in time spectrum. Different from traditional methods such as time domain cancellation and subspace decomposition
this method can be used to implement sea clutter suppression through node classification of different signals in the time spectrum. Simulation and experimental results show that the algorithm can effectively suppress the sea clutter component of radar echo signal
improve the signal to clutter ratio of radar echo signal
and provide a new idea and way for ocean radar to suppress sea clutter.
任红霞 . 雷达海杂波统计建模与仿真 [D ] . 青岛 : 中国海洋大学 , 2015 .
REN H X . Statistical modeling and simulation of radar sea clutter [D ] . Qingdao : Ocean University of China , 2015 . (in Chinese)
丁鹭飞 , 耿富录 . 雷达原理 [M ] . 西安 : 西安电子科技大学出版社 , 2002 .
DING L F , GENG F L . Radar principle [M ] . Xi'an : Xidian University Press , 2002 . (in Chinese)
丁昊 , 董云龙 , 刘宁波 , 等 . 海杂波特性认知研究进展与展望 [J ] . 雷达学报 , 2016 , 5 ( 5 ): 499 - 516 .
DING H , DONG Y L , LIU N B , et al. Research progress and prospects of the cognition of sea zooplankton characteristics [J ] . Journal of Radar , 2016 , 5 ( 5 ): 499 - 516 . (in Chinese)
姜星宇 , 刘宁波 , 丁昊 , 等 . 雷达海杂波时频谱特性分析 [J ] . 海军航空工程学院学报 , 2019 , 34 ( 5 ): 413 - 417 .
JIANG X Y , LIU N B , DING H , et al. Analysis of time-spectrum characteristics of radar sea clutter [J ] . Journal of Naval Aeronautical and Astronautical Engineering , 2019 , 34 ( 5 ): 413 - 417 . (in Chinese)
胡昌华 , 夏启兵 , 周涛 , 等 . 基于MATLAB的系统分析与设计-时频分析 [M ] . 西安 : 西安电子科技大学出版社 , 2002 .
HU C H , XIA Q B , ZHOU T , et al. System analysis and design based on Matlab-time-frequency analysis [M ] . Xi'an : Xidian University Press , 2002 . (in Chinese)
马丽文 , 张金鹏 , 吴家骥 , 等 . 基于门控循环神经网络的海杂波幅度预测 [J ] . 电波科学学报 , 2020 , 35 ( 2 ): 257 - 263 .
MA L W , ZHANG J P , WU J J , et al. Prediction of sea clutter amplitude based on gated cyclic neural network [J ] . Chinese Journal of Radio Science , 2020 , 35 ( 2 ): 257 - 263 . (in Chinese)
何耀民 , 何华锋 , 徐永壮 , 等 . 基于反向传播神经网络的海杂波参数估计 [J ] . 兵工学报 , 2019 , 40 ( 12 ): 2473 - 2481 . DOI: 10.3969/j.issn.1000-1093.2019.12.011 http://doi.org/10.3969/j.issn.1000-1093.2019.12.011 研究分析海杂波、评估弹载导引头在不同海况下的打击精度具有重大意义。针对传统统计方法的海杂波参数估计易存在脱离实况海杂波物理特征的问题,提出基于反向传播(BP)神经网络的参数估计法。利用海杂波幅度分布特性和时间相关性,建立基于K分布的时间与空间相关海杂波模型;重点分析形状参数、尺度参数、杂波速度均方根、平均多普勒频移4个模型参数对海杂波混沌特性、分形特性的影响,总结出模型参数与物理特征之间的定性关系;利用BP神经网络充分挖掘参数与物理特征间的定量关系,并对混沌特性、分形特性进行预测,决定系数为0.985、0.952. 以实测海杂波数据为例,比较BP神经网络、最大似然估计和矩估计法的模型参数,验证了该方法可以较好地贴近真实海杂波的物理特征。
HE Y M , HE H F , XU Y Z , et al. Sea clash parameter estimation based on back-propagation neural network [J ] . Acta Armamentarii , 2019 , 40 ( 12 ): 2473 - 2481 . (in Chinese)
许桂桂 . 基于深度学习的海面弱目标探测技术研究 [D ] . 武汉 : 华中科技大学 , 2019 .
XU G G . Research on weak target detection technology in sea surface based on deep learning [D ] . Wuhan : Huazhong University of Science and Technology , 2019 . (in Chinese)
施赛楠 , 董泽远 , 杨静 , 等 . 基于时频图自主学习的海面小目标检测 [J ] . 系统工程与电子技术 , 2021 , 43 ( 1 ): 33 - 41 .
SHI S N , DONG Z Y , YANG J , et al. Small target detection in sea surface based on time-frequency graph autonomous learning [J ] . Systems Engineering and Electronics , 2021 , 43 ( 1 ): 33 - 41 . (in Chinese)
潘美艳 , 孙俊 , 杨予昊 , 等 . 基于Faster R-CNN网络的海面目标检测方法 [J ] . 现代雷达 , 2021 , 43 ( 6 ): 19 - 26 .
PAN M Y , SUN J , YANG Y H , et al. Sea surface target detection method based on faster R-CNN network [J ] . Modern Radar , 2021 , 43 ( 6 ): 19 - 26 . (in Chinese)
CHEN Z Z , HE C , ZHAO C , et al. Using SVD-FRFT filtering to suppress first-order sea clutter in HFSWR [J ] . IEEE Geoscience and Remote Sensing Letters , 2017 , 14 ( 7 ): 1076 - 1080 . DOI: 10.1109/LGRS.2017.2697458 http://doi.org/10.1109/LGRS.2017.2697458 http://ieeexplore.ieee.org/document/7927710/ http://ieeexplore.ieee.org/document/7927710/
王龙岗 , 岳显昌 , 吴雄斌 , 等 . 基于奇异值分解的空域海杂波抑制算法 [J ] . 电波科学学报 , 2021 , 36 ( 4 ): 645 - 652 .
WANG L G , YUE X C , WU X B , et al. Spatial sea clutter suppression algorithm based on singular value decomposition [J ] . Chinese Journal of Radio Science , 2021 , 36 ( 4 ): 645 - 652 . (in Chinese)
孙国政 , 卞雷祥 . 雷达杂波自适应抑制技术 [J ] . 雷达与对抗 , 2011 , 31 ( 2 ): 11 - 13 .
SUN G Z , BIAN L X . Radar clutter adaptive suppression technology [J ] . Radar and Countermeasure , 2011 , 31 ( 2 ): 11 - 13 . (in Chinese)
唐先慧 , 李东 , 粟嘉 , 等 . 基于AlexNet的自适应杂波智能抑制方法 [J ] . 信号处理 , 2020 , 36 ( 12 ): 2032 - 2042 .
TANG X H , LI D , SU J , et al. Adaptive clash suppression based on AlexNet [J ] . Signal Processing , 2020 , 36 ( 12 ): 2032 - 2042 . (in Chinese)
BRUNA J , ZAREMBA W , SZLAM A , et al. Spectral networks and locally connected networks on graphs [J ] . Computer Science , 2013 , 29 ( 11 ): 1 - 14 . DOI: 10.1063/1.30584 http://doi.org/10.1063/1.30584 http://aip.scitation.org/doi/abs/10.1063/1.30584 http://aip.scitation.org/doi/abs/10.1063/1.30584
MIKOLOV T , CHEN K , CORRADO G , et al. Efficient estimation of word representations in vector space [C ] // Proceedings of the 1st International Conference on Learning Representations.Scottsdale,AZ,US : 2013 : 1 - 9 .
Grover Aditya and Leskovec Jure . node2vec: Scalable feature learning for networks [C ] // Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.San Francisco,CA,US:ACM , 2016 : 855 - 864 .
车转转 . 海杂波中目标检测的时频方法研究 [D ] . 西安 : 西安电子科技大学 , 2014 .
CHE Z Z . Research on time-frequency method of target detection in sea cluttered [D ] . Xi'an : Xidian University , 2014 . (in Chinese)
PEROZZI B , AI-RFOU R , SKIENA S . Deepwalk:online learning of social representations [C ] // Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York,NY,US:ACM , 2014 : 701 - 710 .
TANG J , QU M , WANG M , et al. LINE:Large-scale information network embedding [C ] // Proceedings of the 24th International Conference on World Wide Web.Florence,Italy:ACM , 2015 : 1067 - 1077 .
杨晋吉 , 胡波 , 王欣明 , 等 . 一种知识图谱的排序学习个性化推荐算法 [J ] . 小型微型计算机系统 , 2018 , 39 ( 11 ): 2419 - 2423 .
YANG J J , HU B , WANG X M , et al. A sorting learning personalized recommendation algorithm for knowledge graph [J ] . Journal of Small Microcomputer Systems , 2018 , 39 ( 11 ): 2419 - 2423 . (in Chinese)
雷雨 . 用户属性信息推断方法的研究 [D ] . 哈尔滨 : 哈尔滨工业大学 , 2019 .
LEI Y . Research on user attribute information inference method [D ] . Harb in:Harbin Institute of Technology, 2019 . (in Chinese)
刘宇 , 顾振杰 , 韩红斌 . 基于实测数据的海杂波建模方法 [J ] . 兵工自动化 , 2019 , 38 ( 9 ): 64 - 67 .
LIU Y , GU Z J , HAN H B . Sea clutter modeling method based on measured data [J ] . Ordnance Industry Automation , 2019 , 38 ( 9 ): 64 - 67 . (in Chinese)
刘宁波 , 丁昊 , 黄勇 , 等 . X波段雷达对海探测试验与数据获取年度进展 [J ] . 雷达学报 , 2021 , 10 ( 1 ): 173 - 182 .
LIU N B , DING H , HUANG Y , et al. Annual progress of X-band radar sea-to-sea detection experiment and data acquisition [J ] . Acta Radar Sinica , 2021 , 10 ( 1 ): 173 - 182 . (in Chinese)
关泽文 , 陈建文 , 鲍拯 . 一种改进的基于峰值信噪比-高阶奇异值分解的天波超视距雷达自适应海杂波抑制算法 [J ] . 电子与信息学报 , 2019 , 41 ( 7 ): 1743 - 1750 .
GUAN Z W , CHEN J W , BAO Z . An improved adaptive sea clutter suppression algorithm for sky-wave over-the-horizon radar based on peak signal-to-noise ratio and high-order singular value decomposition [J ] . Journal of Electronics & Information Technology , 2019 , 41 ( 7 ): 1743 - 1750 . (in Chinese)
0
Views
214
下载量
0
CNKI被引量
Publicity Resources
Related Articles
Related Author
Related Institution
京公网安备11010802024360号