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基于强散射点在线估计的距离扩展目标检测方法

郭鹏程 刘峥 罗丁利 李俭朴

引用本文: 郭鹏程, 刘峥, 罗丁利, 李俭朴. 基于强散射点在线估计的距离扩展目标检测方法[J]. 电子与信息学报, 2020, 42(4): 910-916. doi: 10.11999/JEIT190417 shu
Citation:  Pengcheng GUO, Zheng LIU, Dingli LUO, Jianpu LI. Range Spread Target Detection Based on OnlineEstimation of Strong Scattering Points[J]. Journal of Electronics and Information Technology, 2020, 42(4): 910-916. doi: 10.11999/JEIT190417 shu

基于强散射点在线估计的距离扩展目标检测方法

    作者简介: 郭鹏程: 男,1983年生,高级工程师,博士生,研究方向为雷达目标检测与识别;
    刘峥: 男,1964年生,教授,研究方向为雷达信号处理的理论与系统设计、雷达精确制导技术、多传感器融合等;
    罗丁利: 男,1974年生,研究员,研究方向为雷达信号处理、目标分类识别技术;
    李俭朴: 男,1994年生,硕士生,研究方向为雷达目标检测
    通讯作者: 刘峥,lz@xidian.edu.cn
摘要: 传统的距离扩展目标检测一般在散射点密度或散射点数量先验条件下完成,在目标散射点信息完全未知时检测性能会大幅降低。针对这个问题,该文提出一种基于强散射点在线估计的距离扩展目标检测方法(OESS-RSTD),该方法利用机器学习中的无监督聚类算法在线估计强散射点数量以及首次检测门限,然后再结合虚警率,确定2次检测门限,最后通过两次门限检测完成目标有无的判决。该文分别利用仿真数据和实测数据进行了试验验证,并和其他算法进行了试验对比,通过虚警概率一定时的信噪比(SNR)-检测概率曲线验证了该文所提方法相对于传统算法有更高的稳健性,且该方法不需要目标散射点的任何先验信息。

English

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    1. [1]

      缪祥华, 单小撤. 基于密集连接卷积神经网络的入侵检测技术研究. 电子与信息学报, 2020, 41(0): 1-7.

  • 图 1  检测器各区域的示意图

    图 2  非先验依赖的扩展目标检测流程图

    图 3  卡车典型姿态的高分辨距离像

    图 4  基于4种仿真模型的检测性能对比

    图 5  基于实测数据的检测性能对比结果

    表 1  4种典型散射点模型

    编号散射点分布特点名称
    模型11个强散射点,占全部能量单散射点
    模型210个散射点,一个强散射点占50%能量,其他散射点占各占5.556%能量稀疏多散射点
    模型332个散射点,两个强散射点各占25%,其他散射点占各占1.66%能量密集非均匀多散射点
    模型432个散射点,均匀分布,各占3.125%能量密集均匀散射点
    下载: 导出CSV
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文章相关
  • 通讯作者:  刘峥, lz@xidian.edu.cn
  • 收稿日期:  2019-06-06
  • 录用日期:  2019-09-07
  • 网络出版日期:  2019-09-19
  • 刊出日期:  2020-04-01
通讯作者: 陈斌, bchen63@163.com
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