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海雜波AR譜多重分形特性及微弱目標檢測方法

范一飛 羅豐 李明 胡沖 陳帥霖

范一飛, 羅豐, 李明, 胡沖, 陳帥霖. 海雜波AR譜多重分形特性及微弱目標檢測方法[J]. 電子與信息學報, 2016, 38(2): 455-463. doi: 10.11999/JEIT150581
引用本文: 范一飛, 羅豐, 李明, 胡沖, 陳帥霖. 海雜波AR譜多重分形特性及微弱目標檢測方法[J]. 電子與信息學報, 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

海雜波AR譜多重分形特性及微弱目標檢測方法

doi: 10.11999/JEIT150581
基金項目: 

國家部委基金(4010101030101)

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

Funds: 

The National Ministries Fund (4010101030101)

  • 摘要: 該文研究了海雜波功率譜的多重分形特性。為了克服頻譜傅里葉分析的缺點,用現(xiàn)代譜估計的方法來計算海雜波的功率譜。AR模型是一個線性預測模型,它通過序列的自相關函數(shù)矩陣來估計功率譜,并且具有更精確的頻譜分辨率。該文主要分析基于AR譜估計的海雜波功率譜的多重分形特性,以及在微弱目標檢測中的應用。首先,以分數(shù)布朗運動(FBM)模型為例,證明其功率譜具有多重分形特性。其次,根據X波段雷達的實測海雜波數(shù)據,通過多重去趨勢分析法(MF-DFA)驗證了海雜波AR譜的多重分形特性。最后,分析了海雜波AR譜的廣義Hurst指數(shù)以及影響參數(shù),并提出一種基于局部AR譜廣義Hurst指數(shù)的目標檢測方法。實驗結果表明,該種檢測方法具有海雜波背景下微弱目標檢測的能力。與現(xiàn)有的分形檢測方法和傳統(tǒng)的CFAR檢測方法對比,該算法在低信雜比情況下具有較好的檢測性能。
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  • 文章訪問數(shù):  1483
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  • 被引次數(shù): 0
出版歷程
  • 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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