逆Gamma纹理背景下两类子空间目标的自适应检测方法

丁昊 王国庆 刘宁波 关键

丁昊, 王国庆, 刘宁波, 关键. 逆Gamma纹理背景下两类子空间目标的自适应检测方法[J]. 雷达学报, 2017, 6(3): 275-284. doi: 10.12000/JR16088
引用本文: 丁昊, 王国庆, 刘宁波, 关键. 逆Gamma纹理背景下两类子空间目标的自适应检测方法[J]. 雷达学报, 2017, 6(3): 275-284. doi: 10.12000/JR16088
Ding Hao, Wang Guoqing, Liu Ningbo, Guan Jian. Adaptive Detectors for Two Types of Subspace Targets in an Inverse Gamma Textured Background[J]. Journal of Radars, 2017, 6(3): 275-284. doi: 10.12000/JR16088
Citation: Ding Hao, Wang Guoqing, Liu Ningbo, Guan Jian. Adaptive Detectors for Two Types of Subspace Targets in an Inverse Gamma Textured Background[J]. Journal of Radars, 2017, 6(3): 275-284. doi: 10.12000/JR16088

逆Gamma纹理背景下两类子空间目标的自适应检测方法

doi: 10.12000/JR16088
基金项目: 

国家自然科学基金 61531020

国家自然科学基金 61501487

山东省自然科学基金 2015ZRA06052

国家自然科学基金 61471381

国家自然科学基金 61471382

国家自然科学基金 61401495

航空科学基金 20150184003

详细信息
    作者简介:

    丁昊(1988-),男,博士研究生,主要研究方向为海杂波特性认知、雷达目标检测等。E-mail: hao3431@tom.com

    王国庆(1980-),男,博士,讲师,主要研究方向为高速信号采集、雷达信号处理等。E-mail: gqwang80@126.com

    刘宁波(1983-),男,博士,讲师,研究方向为雷达信号处理、海杂波中目标的非线性检测。E-mail: lnb198300@163.com

    关键(1968-),男,教授,博士生导师,获全国优秀博士学位论文奖,新世纪百千万人才工程国家级人选。主要研究方向为雷达目标检测与跟踪、侦察图像处理和信息融合。E-mail: guanjian96@tsinghua.org.cn

    通讯作者:

    丁昊, E-mail: hao3431@tom.com

    关键, E-mail: guanjian96@tsinghua.org.cn

  • 中图分类号: TN957

Adaptive Detectors for Two Types of Subspace Targets in an Inverse Gamma Textured Background

Funds: 

Foundation Items: The National Natural Science Foundation of China 61531020

Foundation Items: The National Natural Science Foundation of China 61501487

The Natural Science Foundation of Shandong 2015ZRA06052

Foundation Items: The National Natural Science Foundation of China 61471381

Foundation Items: The National Natural Science Foundation of China 61471382

Foundation Items: The National Natural Science Foundation of China 61401495

The Aeronautical Science Foundation of China 20150184003

  • 摘要: 该文在复合高斯海杂波背景下,以逆Gamma分布作为纹理分量的先验分布模型,研究了1阶高斯(First Order Gaussian, FOG)和2阶高斯(Second Order Gaussian, SOG)两类子空间目标的自适应检测问题。采用两步广义似然比(Generalized Likelihood Ratio Test, GLRT)推导了检测统计量,并分别采用采样协方差矩阵(Sample Covariance Matrix, SCM)、归一化采样协方差矩阵(Normalized Sample Covariance Matrix, NSCM)和定点估计(Function Point Estimation, FPE)作为协方差矩阵估计值,与GLRT相结合,构造出新的自适应检测器。由于该文检测器在设计阶段考虑了海杂波的先验分布模型,且在检测阶段采用了与工作环境相匹配的模型参数,经性能分析与验证,其在检测性能上优于已有匹配滤波(Adaptive Matched Filter, AMF)和归一化匹配滤波(Adaptive Normalized Matched Filter, ANMF)检测器。

     

  • 图  1  检测性能对比

    Figure  1.  Comparison of detection performance

    图  2  协方差矩阵估计方法的影响

    Figure  2.  Influence of covariance matrix estimation method

    图  3  参考单元数的影响

    Figure  3.  Influence of reference cell numbers

    图  4  形状参数的影响

    Figure  4.  Influence of shape parameter

    图  5  空间相关性的影响

    Figure  5.  Influence of spatial correlation

    图  6  海杂波幅度分布建模结果

    Figure  6.  Amplitude distribution modeling results of sea clutter

    图  7  实测海杂波中的检测性能

    Figure  7.  Detection performance in measured sea clutter

    图  8  检测性能随目标多普勒频率的变化曲线

    Figure  8.  Variation of detection performance with the target's Doppler frequency

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出版历程
  • 收稿日期:  2016-07-18
  • 修回日期:  2016-10-24
  • 网络出版日期:  2017-06-28

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