[1]黎 炎,李 哲,陈 扬,等.训练样本不足时雷达扩展目标检测方法[J].南京理工大学学报(自然科学版),2018,42(06):727.[doi:10.14177/j.cnki.32-1397n.2018.42.06.014]
 Li Yan,Li Zhe,Chen Yang,et al.Radar range-spread target detection with limited training data[J].Journal of Nanjing University of Science and Technology,2018,42(06):727.[doi:10.14177/j.cnki.32-1397n.2018.42.06.014]
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训练样本不足时雷达扩展目标检测方法()
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《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
42卷
期数:
2018年06期
页码:
727
栏目:
出版日期:
2018-12-30

文章信息/Info

Title:
Radar range-spread target detection with limited training data
文章编号:
1005-9830(2018)06-0727-05
作者:
黎 炎123李 哲123陈 扬123王 剑4胡丹晖5吴 驰6
1.南瑞集团有限公司,江苏 南京211106; 2.国网电力科学研究院武汉南瑞有限责任公司,湖北 武汉 430074; 3.电网雷击风险预防湖北省重点实验室,湖北 武汉430074; 4.国家电网有限公司,北京 100031; 5.国网湖北省电力公司,湖北 武汉 430074; 6.国网四川省电力公司,四川 成都 610041
Author(s):
Li Yan123Li Zhe123Chen Yang123Wang Jian4Hu Danhui5Wu Chi6
1.NARI Group Corporation,Nanjing 211106,China; 2.Wuhan NARI Limited LiabilityCompany of State Grid Electric Power Research Institute,Wuhan 430074,China; 3.Hubei Key Laboratory of Power Grid Lightning Risk Prevention,Wuhan 430074,China; 4.State Grid Corporation of China,Beijing 100031,China; 5.State Grid Hubei Electric Power Company,Wuhan 430074,China; 6.State Grid Sichuan Electric Power Company,Chengdu 610041,China
关键词:
雷达 扩展目标 信号检测 降秩 广义似然比检验
Keywords:
radars range-spread targets signal detection reduced-rank generalized likelihood ratio test
分类号:
TN957
DOI:
10.14177/j.cnki.32-1397n.2018.42.06.014
摘要:
为了降低对训练样本的需求,针对雷达扩展目标检测问题,该文提出了降秩广义似然比检验(R-GLRT)检测器和降秩Wald(R-Wald)检测器。利用噪声子空间对应的特征矩阵代替采样协方差矩阵,降低了训练样本不足时小训练样本带来的估计误差。仿真结果表明,当训练样本不足时,所提出的降秩检测器能够提供较高的检测概率,且R-GLRT检测器具有比R-Wald检测器更高的检测概率; 当训练样本充足时,与常规自适应检测器相比,2种降秩检测器也能够提供较高的检测概率。
Abstract:
A reduced-rank generalized likelihood ratio test(R-GLRT)detector and a reduced-rank Wald(R-Wald)detector are proposed for radar range-spread target detection to reduce the requirement of training samples. The sampling covariance matrix is replaced by a characteristic matrix corresponding to the noise subspace,and the estimating error is reduced for low sample support. Simulation results show that the proposed reduced-rank detectors can work properly with low sample support,and the detection performance of the R-GLRT detector is better than that of the R-Wald detector; the detection performance of the proposed reduced-rank detectors is better than that of conventional adaptive detector with sufficient training data.

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 Zhou Dequan Liu Guosui Wang Jianxin.Study of Technology of Radar Target Identification Based on Hidden Markov Model[J].Journal of Nanjing University of Science and Technology,1998,(06):32.
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备注/Memo

备注/Memo:
收稿日期:2018-10-24 修回日期:2018-11-12
基金项目:国家电网公司科技项目(524625160016)
作者简介:黎炎(1990-),男,硕士,工程师,主要研究方向:电子与通信工程,E-mail:592672152@qq.com。
引文格式:黎炎,李哲,陈扬,等. 训练样本不足时雷达扩展目标检测方法[J]. 南京理工大学学报,2018,42(6):727-731.
投稿网址:http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2018-12-30