摘要
密集转发干扰与雷达发射信号高度相关,兼具压制式和欺骗式干扰效果,使雷达系统难以检测到真实目标,严重威胁雷达作战能力。针对这一问题,该文提出一种基于支持向量机(SVM)的捷变频雷达密集转发干扰智能抑制方法。通过对随机样本集进行离线训练获得最优SVM模型,智能化识别并分类目标和干扰;然后,采用平滑滤波进一步抑制目标所在距离单元内的干扰信号;最后,基于压缩感知(CS)理论进行二维高分辨重构,估计出目标参数信息。仿真实验与实测数据处理结果表明,所提算法在不同场景下均能够有效抑制密集转发干扰,准确检测出真实目标。
Dense-repeated jamming is highly related to the radar-transmitted signal,and it has suppression and deception jamming effects,which makes detecting the real target difficult for a radar system and seriously threatens the operational capability of radar.To solve this problem,an intelligent suppression method based on the Support Vector Machine(SVM)is proposed in this paper.The optimal SVM model is obtained through offline training on a random sample set to intelligently identify and classify targets and interference.Then,the interference sidelobe in the target range unit is further suppressed by smoothing filtering.Finally,highresolution two-dimensional reconstruction is performed based on Compress Sensing(CS)theory to estimate the target parameter information.Simulation experiments and measured data processing results reveal that the proposed algorithm can effectively suppress dense-repeated jamming and accurately detect real targets in different scenarios.
作者
杜思予
刘智星
吴耀君
沙明辉
全英汇
DU Siyu;LIU Zhixing;WU Yaojun;SHA Minghui;QUAN Yinghui(School of Electronic Engineering,Xidian University,Xi’an 710071,China;Beijing Institute of Radio Measurement,Beijing 100854,China)
出处
《雷达学报（中英文）》
EI
CSCD
北大核心
2023年第1期173-185,共13页
Journal of Radars
基金
国家自然科学基金(61772397)
陕西省杰出青年科学基金(2021JC-23)
陕西省科技创新团队(2019TD-002)。
关键词
电子对抗
机器学习
捷变频雷达
密集转发干扰
支持向量机
Electronic Counter-Measures(ECM)
Machine learning
Frequency agility radar
Dense repeated jamming
Support Vector Machine(SVM)
