摘要
提出了一种强主瓣压制干扰环境下的雷达目标距离–角度联合稀疏估计方法,目的在于有效抑制多个强主瓣压制干扰,同时提取目标的距离和空间角度信息.新方法首先将雷达天线面阵的每一行(列)接收数据进行空域自适应处理,可在一维上有效抑制多个强主瓣压制干扰,并保留另一维的目标角度信息,然后利用基于稀疏Bayes学习的距离–角度联合估计算法提取目标的距离和俯仰角(方位角)信息.新方法在系统幅相误差及频带误差所带来的干扰抑制剩余情况下同样有效,具有较强的鲁棒性.最后,通过性能分析给出了新方法对干扰与目标之间夹角、目标信噪比的适用条件.
In this paper,a sparse estimation algorithm for the radar target range and direction under strong mainlobe blanket jamming conditions is presented.This algorithm can effectively suppress multiple strong mainlobe blanket jammings (multi-MLJs)and extract the information about target distance and direction.First, the space domain adaptive processing for the received signal in each row (or column)is adopted to suppress multi-MLJs.Second,the sparse Bayesian learning is employed to obtain the information of the target distance and elevation (or azimuth).In addition,the proposed method still has an effective performance under the error condition and the applicable conditions of the new algorithm based on the performance analysis are provided in this paper.
作者
周必雷
段克清
李荣锋
王永良
戴凌燕
陈风波
Bilei ZHOU;Keqing DUAN;Rongfeng LI;Yongliang WANG;Lingyan DAI;Fengbo CHEN(Department of Early Warning Technology,Air Force Early Warning Academy,Wuhan 430019,China)
出处
《中国科学:信息科学》
CSCD
北大核心
2018年第12期1634-1650,共17页
Scientia Sinica(Informationis)
基金
国家自然科学基金(批准号:61501506
61501505)资助项目
关键词
主瓣压制干扰
空域自适应处理
稀疏Bayes学习
雷达
抗干扰
mainlobe blanket jammings (MLJ)
space domain adaptive processing
sparse Bayesian learning
radar
anti-jamming