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海杂波AR谱多重分形特性及微弱目标检测方法

范一飞 罗丰 李明 胡冲 陈帅霖

范一飞, 罗丰, 李明, 胡冲, 陈帅霖. 海杂波AR谱多重分形特性及微弱目标检测方法[J]. 电子与信息学报, 2016, 38(2): 455-463. doi: 10.11999/JEIT150581
引用本文: 范一飞, 罗丰, 李明, 胡冲, 陈帅霖. 海杂波AR谱多重分形特性及微弱目标检测方法[J]. 电子与信息学报, 2016, 38(2): 455-463. doi: 10.11999/JEIT150581
FAN Yifei, LUO Feng, LI Ming, HU Chong, CHEN Shuailin. The Multifractal Properties of AR Spectrum and Weak Target Detection in Sea Clutter Background[J]. Journal of Electronics & Information Technology, 2016, 38(2): 455-463. doi: 10.11999/JEIT150581
Citation: FAN Yifei, LUO Feng, LI Ming, HU Chong, CHEN Shuailin. The Multifractal Properties of AR Spectrum and Weak Target Detection in Sea Clutter Background[J]. Journal of Electronics & Information Technology, 2016, 38(2): 455-463. doi: 10.11999/JEIT150581

海杂波AR谱多重分形特性及微弱目标检测方法

doi: 10.11999/JEIT150581
基金项目: 

国家部委基金(4010101030101)

The Multifractal Properties of AR Spectrum and Weak Target Detection in Sea Clutter Background

Funds: 

The National Ministries Fund (4010101030101)

  • 摘要: 该文研究了海杂波功率谱的多重分形特性。为了克服频谱傅里叶分析的缺点,用现代谱估计的方法来计算海杂波的功率谱。AR模型是一个线性预测模型,它通过序列的自相关函数矩阵来估计功率谱,并且具有更精确的频谱分辨率。该文主要分析基于AR谱估计的海杂波功率谱的多重分形特性,以及在微弱目标检测中的应用。首先,以分数布朗运动(FBM)模型为例,证明其功率谱具有多重分形特性。其次,根据X波段雷达的实测海杂波数据,通过多重去趋势分析法(MF-DFA)验证了海杂波AR谱的多重分形特性。最后,分析了海杂波AR谱的广义Hurst指数以及影响参数,并提出一种基于局部AR谱广义Hurst指数的目标检测方法。实验结果表明,该种检测方法具有海杂波背景下微弱目标检测的能力。与现有的分形检测方法和传统的CFAR检测方法对比,该算法在低信杂比情况下具有较好的检测性能。
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出版历程
  • 收稿日期:  2015-05-15
  • 修回日期:  2015-10-13
  • 刊出日期:  2016-02-19

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