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复合高斯杂波条件下存在干扰时的反对称贝叶斯检测器

杨海峰 李振兴 胡晓琴 李琼 狄源水

杨海峰, 李振兴, 胡晓琴, 李琼, 狄源水. 复合高斯杂波条件下存在干扰时的反对称贝叶斯检测器[J]. 电子与信息学报, 2022, 44(9): 3163-3169. doi: 10.11999/JEIT210690
引用本文: 杨海峰, 李振兴, 胡晓琴, 李琼, 狄源水. 复合高斯杂波条件下存在干扰时的反对称贝叶斯检测器[J]. 电子与信息学报, 2022, 44(9): 3163-3169. doi: 10.11999/JEIT210690
YANG Haifeng, LI Zhenxing, HU Xiaoqin, LI Qiong, DI Yuanshui. Persymmetric Bayesian Detector in Compound Gaussian Clutter and Jamming[J]. Journal of Electronics & Information Technology, 2022, 44(9): 3163-3169. doi: 10.11999/JEIT210690
Citation: YANG Haifeng, LI Zhenxing, HU Xiaoqin, LI Qiong, DI Yuanshui. Persymmetric Bayesian Detector in Compound Gaussian Clutter and Jamming[J]. Journal of Electronics & Information Technology, 2022, 44(9): 3163-3169. doi: 10.11999/JEIT210690

复合高斯杂波条件下存在干扰时的反对称贝叶斯检测器

doi: 10.11999/JEIT210690
详细信息
    作者简介:

    杨海峰:男,讲师,研究方向为雷达自适应检测、阵列信号处理

    李振兴:男,助教,研究方向为阵列信号处理

    胡晓琴:女,副教授,研究方向为阵列信号处理、空间谱估计

    李琼:女,讲师,研究方向为雷达接收系统

    狄源水:男,高级工程师,研究方向为雷达系统、雷达信号处理

    通讯作者:

    杨海峰 18672954451@163.com

  • 中图分类号: TN928

Persymmetric Bayesian Detector in Compound Gaussian Clutter and Jamming

  • 摘要: 该文对复合高斯杂波条件下存在干扰时的目标检测问题进行研究。针对自适应检测器需要一定数目独立同分布训练样本才能保证较好的检测性能,利用接收天线的反对称结构以及引入杂波协方差矩阵的先验信息的方法,利用两步广义似然比检验准则提出了该背景下的贝叶斯检测器。仿真结果表明,该文提出的检测器在训练样本数较少时具有较好的目标检测性能。
  • 图  1  $\mu_r $=16, JNR=30 dB不同检测器的检测概率曲线

    图  2  $\mu_r $=16, JNR=30 dB不同检测器的检测概率曲线

    图  3  L=5, JNR=30 dB不同检测器的检测概率曲线

    图  4  L=5, $\mu_r $=17, SNR=1 dB不同检测器的检测概率曲线

    图  5  L=10不同检测器不同杂波分布下的检测概率曲线

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出版历程
  • 收稿日期:  2021-07-09
  • 修回日期:  2022-03-27
  • 网络出版日期:  2022-04-15
  • 刊出日期:  2022-09-19

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