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基于高斯化-廣義匹配的脈沖型噪聲處理方法研究

羅忠濤 盧鵬 張楊勇 張剛

羅忠濤, 盧鵬, 張楊勇, 張剛. 基于高斯化-廣義匹配的脈沖型噪聲處理方法研究[J]. 電子與信息學(xué)報(bào), 2018, 40(12): 2928-2935. doi: 10.11999/JEIT180191
引用本文: 羅忠濤, 盧鵬, 張楊勇, 張剛. 基于高斯化-廣義匹配的脈沖型噪聲處理方法研究[J]. 電子與信息學(xué)報(bào), 2018, 40(12): 2928-2935. doi: 10.11999/JEIT180191
Zhongtao LUO, Peng LU, Yangyong ZHANG, Gang ZHANG. A Novel Method for Nonlinear Processing in Impulsive Noise Based on Gaussianization and Generalized Matching[J]. Journal of Electronics & Information Technology, 2018, 40(12): 2928-2935. doi: 10.11999/JEIT180191
Citation: Zhongtao LUO, Peng LU, Yangyong ZHANG, Gang ZHANG. A Novel Method for Nonlinear Processing in Impulsive Noise Based on Gaussianization and Generalized Matching[J]. Journal of Electronics & Information Technology, 2018, 40(12): 2928-2935. doi: 10.11999/JEIT180191

基于高斯化-廣義匹配的脈沖型噪聲處理方法研究

doi: 10.11999/JEIT180191
基金項(xiàng)目: 國(guó)家自然科學(xué)基金(61701067, 61771085, 61671095),重慶市教育委員會(huì)科研基金(KJ1600427, KJ1600429)
詳細(xì)信息
    作者簡(jiǎn)介:

    羅忠濤:男,1984年生,講師,碩士生導(dǎo)師,研究方向?yàn)榻y(tǒng)計(jì)信號(hào)處理與數(shù)字圖像處理

    盧鵬:男,1994年生,碩士生,研究方向?yàn)榈皖l噪聲分析與低頻通信信號(hào)處理

    張楊勇:男,1983年生,高級(jí)工程師,研究方向?yàn)榈皖l通信技術(shù)與信號(hào)處理

    張剛:男,1976年生,副教授,碩士生導(dǎo)師,研究方向?yàn)槲⑷跣盘?hào)檢測(cè)與混沌信號(hào)處理

    通訊作者:

    羅忠濤  luozt@cqupt.edu.cn

  • 中圖分類(lèi)號(hào): TN911

A Novel Method for Nonlinear Processing in Impulsive Noise Based on Gaussianization and Generalized Matching

Funds: The National Natural Science Foundation of China (61701067, 61771085, 61671095), The Project supported by Scientific Research Foundation of the Chongqing Education Committee (KJ1600427, KJ1600429)
  • 摘要: 針對(duì)脈沖型噪聲,該文提出一種新的非線性處理方法,即高斯化-廣義匹配(GGM)處理。GGM方法基于高斯化處理與廣義匹配濾波,可結(jié)合非參數(shù)的概率密度估計(jì)進(jìn)行設(shè)計(jì),解決噪聲模型未知時(shí)的非線性處理問(wèn)題。該文以脈沖型噪聲 ${\rm S\alpha S}$ 分布模型為例,分析GGM方法的特點(diǎn)和性能;再結(jié)合Class A噪聲模型,討論GGM設(shè)計(jì)作為非參數(shù)方法相比模型假設(shè)失配的優(yōu)勢(shì);引入效能函數(shù),驗(yàn)證GGM方法在恒虛警技術(shù)中的運(yùn)用。結(jié)果表明,在已知噪聲分布情況下,GGM方法具有次優(yōu)檢測(cè)性能;當(dāng)噪聲模型未知時(shí),非參數(shù)GGM設(shè)計(jì)能保持穩(wěn)健性能,優(yōu)于模型失配下的處理。并且,GGM設(shè)計(jì)對(duì)樣本數(shù)目要求不高,為噪聲特性不明或時(shí)變的場(chǎng)景提供了一種新的信號(hào)處理方法。
  • 圖  1  基于PDF或樣本的GGM函數(shù)

    圖  2  針對(duì) ${\rm S\alpha S}$ 模型的ZMNL函數(shù), $\alpha $ =1.5, $\gamma $ =1

    圖  3  ${\rm S\alpha S}$ 噪聲中不同ZMNL函數(shù)的效能, $\gamma $ =1

    圖  4  基于不同樣本數(shù)目的GGM設(shè)計(jì)的效能, $\gamma $ =1

    圖  5  Class A噪聲中GGM方法的效能, ${Γ} $ =10–3

    圖  6  Class A噪聲中的恒虛警性能,a=0.1, ${Γ} $ =10–3

    圖  7  實(shí)測(cè)大氣噪聲下的恒虛警性能

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出版歷程
  • 收稿日期:  2018-02-11
  • 修回日期:  2018-07-26
  • 網(wǎng)絡(luò)出版日期:  2018-08-03
  • 刊出日期:  2018-12-01

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