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一种基于多尺度核学习的仿射投影滤波算法

李群生 赵剡 寇磊 王进达

引用本文: 李群生, 赵剡, 寇磊, 王进达. 一种基于多尺度核学习的仿射投影滤波算法[J]. 电子与信息学报, doi: 10.11999/JEIT190023 shu
Citation:  Qunsheng LI, Yan ZHAO, Lei KOU, Jinda WANG. An Affine Projection Algorithm with Multi-scale Kernels Learning[J]. Journal of Electronics and Information Technology, doi: 10.11999/JEIT190023 shu

一种基于多尺度核学习的仿射投影滤波算法

    作者简介: 李群生: 男,1977年生,博士,研究方向为滤波信号处理,组合导航技术;
    赵剡: 男,1956年生,教授,研究方向为惯性技术,信号处理技术;
    寇磊: 男,1989年生,博士,研究方向为滤波信号处理,组合导航技术;
    王进达: 女,1971年生,高级工程师,研究方向为惯性技术
    通讯作者: 李群生,570658391@qq.com
  • 基金项目: 国家自然科学基金(61233005),航空基金(20160812004, 20160112002, 2016ZA12002)

摘要: 为了提高强非线性信号的噪声消除和信道均衡能力,在核学习自适应滤波方法的基础上,该文提出一种基于惊奇准则的多尺度核学习仿射投影滤波方法。在核仿射投影滤波算法的基础上,对核组合函数结构进行改进,将多个不同高斯核带宽作为可变参数,与加权系数共同参与滤波器的更新;利用惊奇准则将计算结果稀疏化,根据仿射投影算法的约束条件对惊奇测度进行改进,简化其方差项,降低了计算的复杂度。将该算法应用于噪声消除、信道均衡以及MG时间序列预测中,与多种自适应滤波算法及核学习自适应滤波算法进行仿真结果的对比分析,验证了该算法的优越性。

English

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  • 图 1  滤波器除噪原理

    图 2  噪声分布

    图 3  对数条件下MSE的学习曲线

    图 4  对数条件下MSE的学习曲线

    图 5  MG时间序列的预测学习曲线

    表 1  算法参数

    算法核带宽收敛因子正则化参数$\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
    下载: 导出CSV

    表 2  不同高次项下5种方法MMSE(dB)

    高次项$N$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.
    下载: 导出CSV
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文章相关
  • 通讯作者:  李群生, 570658391@qq.com
  • 收稿日期:  2019-01-09
  • 录用日期:  2019-07-30
  • 网络出版日期:  2020-01-11
通讯作者: 陈斌, bchen63@163.com
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