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一種基于多尺度核學(xué)習(xí)的仿射投影濾波算法

李群生 趙剡 寇磊 王進達

李群生, 趙剡, 寇磊, 王進達. 一種基于多尺度核學(xué)習(xí)的仿射投影濾波算法[J]. 電子與信息學(xué)報, 2020, 42(4): 924-931. doi: 10.11999/JEIT190023
引用本文: 李群生, 趙剡, 寇磊, 王進達. 一種基于多尺度核學(xué)習(xí)的仿射投影濾波算法[J]. 電子與信息學(xué)報, 2020, 42(4): 924-931. doi: 10.11999/JEIT190023
Qunsheng LI, Yan ZHAO, Lei KOU, Jinda WANG. An Affine Projection Algorithm with Multi-scale Kernels Learning[J]. Journal of Electronics & Information Technology, 2020, 42(4): 924-931. doi: 10.11999/JEIT190023
Citation: Qunsheng LI, Yan ZHAO, Lei KOU, Jinda WANG. An Affine Projection Algorithm with Multi-scale Kernels Learning[J]. Journal of Electronics & Information Technology, 2020, 42(4): 924-931. doi: 10.11999/JEIT190023

一種基于多尺度核學(xué)習(xí)的仿射投影濾波算法

doi: 10.11999/JEIT190023
基金項目: 國家自然科學(xué)基金(61233005),航空基金(20160812004, 20160112002, 2016ZA12002)
詳細(xì)信息
    作者簡介:

    李群生:男,1977年生,博士,研究方向為濾波信號處理,組合導(dǎo)航技術(shù)

    趙剡:男,1956年生,教授,研究方向為慣性技術(shù),信號處理技術(shù)

    寇磊:女,1971年生,高級工程師,研究方向為慣性技術(shù)

    王進達:男,1989年生,博士,研究方向為濾波信號處理,組合導(dǎo)航技術(shù)

    通訊作者:

    李群生 570658391@qq.com

  • 中圖分類號: TN911.7, TP391

An Affine Projection Algorithm with Multi-scale Kernels Learning

Funds: The National Natural Science Foundation of China (61233005), The Aviation Science Fund (20160812004, 20160112002, 2016ZA12002)
  • 摘要:

    為了提高強非線性信號的噪聲消除和信道均衡能力,在核學(xué)習(xí)自適應(yīng)濾波方法的基礎(chǔ)上,該文提出一種基于驚奇準(zhǔn)則的多尺度核學(xué)習(xí)仿射投影濾波方法(SC-MKAPA)。在核仿射投影濾波算法的基礎(chǔ)上,對核組合函數(shù)結(jié)構(gòu)進行改進,將多個不同高斯核帶寬作為可變參數(shù),與加權(quán)系數(shù)共同參與濾波器的更新;利用驚奇準(zhǔn)則將計算結(jié)果稀疏化,根據(jù)仿射投影算法的約束條件對驚奇測度進行改進,簡化其方差項,降低了計算的復(fù)雜度。將該算法應(yīng)用于噪聲消除、信道均衡以及MG時間序列預(yù)測中,與多種自適應(yīng)濾波算法及核學(xué)習(xí)自適應(yīng)濾波算法進行仿真結(jié)果的對比分析,驗證了該算法的優(yōu)越性。

  • 圖  1  濾波器除噪原理

    圖  2  噪聲分布

    圖  3  對數(shù)條件下MSE的學(xué)習(xí)曲線

    圖  4  對數(shù)條件下MSE的學(xué)習(xí)曲線

    圖  5  MG時間序列的預(yù)測學(xué)習(xí)曲線

    表  1  算法參數(shù)

    算法核帶寬收斂因子正則化參數(shù)$\delta $
    SC-MKAPA${\eta _1} = 1.0$, ${\eta _{\rm{2}}} = {\rm{0}}{\rm{.5}}$, ${\eta _{\rm{3}}} = 1{\rm{0}}$$\mu = 0.2$, $\Delta t = 0.01$5.0×10–3
    NC-MKAPA${\eta _1} = 1.0$, ${\eta _{\rm{2}}} = {\rm{0}}{\rm{.5}}$, ${\eta _{\rm{3}}} = 1{\rm{0}}$$\mu = 0.2$5.0×10–3
    NC-KAPA${\eta _1} = 1.0$$\mu = 0.2$5.0×10–3
    KLMS${\eta _1} = 1.0$$\mu = 0.2$5.0×10–3
    LMS${\eta _1} = 1.0$$\mu = 0.2$5.0×10–3
    下載: 導(dǎo)出CSV

    表  2  不同高次項下5種方法MMSE(dB)

    高次項$N$SC-MKAPANC-MKAPANC-KAPAKLMSLMS
    2–71.2–62.8–67.2–32.7–25.6
    3–62.1–56.9–60.6–24.4–19.3
    6–33.9–29.3–30.2–21.5–17.8
    7–18.3–16.3–15.2–13.3–12.9
    下載: 導(dǎo)出CSV
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出版歷程
  • 收稿日期:  2019-01-09
  • 修回日期:  2019-07-30
  • 網(wǎng)絡(luò)出版日期:  2020-01-11
  • 刊出日期:  2020-06-04

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