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

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

羅忠濤^1, ,,
盧鵬¹,
張楊勇²,
張剛¹

1.
重慶郵電大學(xué)信號(hào)與信息處理重慶市重點(diǎn)實(shí)驗(yàn)室 ??重慶 ??400065
2.
武漢船舶通信研究所 ??武漢 ??430079

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
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- 文章訪問(wèn)數(shù): 1714
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- 被引次數(shù): 0
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

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

1.
Chongqing Key Laboratory of Signal and Information Processing, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
Wuhan Maritime Communication Research Institute, Wuhan 430079, China

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

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。
- 脈沖型噪聲 /
- 非線性處理 /
- 高斯化處理 /
- 廣義匹配濾波 /
- 效能函數(shù)
Abstract: A method based on Gaussianization and generalized matching, called Gaussianization-Generalized Matching (GGM) method is proposed, for nonlinear processing in impulsive noise. The GGM method can be designed based on noise samples, aided by nonparametric probability density estimation. Thus the GGM design is suitable for nonlinear processing in unknown noise models. The GGM method in the ${\rm S\alpha S}$ model is analyzed, and also the comparison with another approach is presented based on unmatched noise model assumption in the Class A noise. The GGM method is applied to the constant false alarm rate technique via the efficacy function. Simulation and analysis results show that the GGM design is sub-optimal, works robustly when the noise model is unknown, and raises a low requirement on the sample number. Thus, the GGM method provides a promising choice when the noise model is unclear or time-varying.
- Impulsive noise /
- Nonlinear processing /
- Gaussianization /
- Generalized matching /
- Efficiency function

HTML全文

圖 1 基于PDF或樣本的GGM函數(shù)

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

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

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

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圖 5 Class A噪聲中GGM方法的效能， ${Γ} $ =10^–3

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

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圖 7 實(shí)測(cè)大氣噪聲下的恒虛警性能

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