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脉冲噪声中基于指数函数的可变拖尾非线性变换设计

罗忠涛 詹燕梅 郭人铭 张杨勇

罗忠涛, 詹燕梅, 郭人铭, 张杨勇. 脉冲噪声中基于指数函数的可变拖尾非线性变换设计[J]. 电子与信息学报, 2020, 42(4): 932-940. doi: 10.11999/JEIT190401
引用本文: 罗忠涛, 詹燕梅, 郭人铭, 张杨勇. 脉冲噪声中基于指数函数的可变拖尾非线性变换设计[J]. 电子与信息学报, 2020, 42(4): 932-940. doi: 10.11999/JEIT190401
Zhongtao LUO, Yanmei ZHAN, Renming GUO, Yangyong ZHANG. Variable Tailing Nonlinear Transformation Design Based on Exponential Function in Impulsive Noise[J]. Journal of Electronics and Information Technology, 2020, 42(4): 932-940. doi: 10.11999/JEIT190401
Citation: Zhongtao LUO, Yanmei ZHAN, Renming GUO, Yangyong ZHANG. Variable Tailing Nonlinear Transformation Design Based on Exponential Function in Impulsive Noise[J]. Journal of Electronics and Information Technology, 2020, 42(4): 932-940. doi: 10.11999/JEIT190401

脉冲噪声中基于指数函数的可变拖尾非线性变换设计

doi: 10.11999/JEIT190401
基金项目: 国家自然科学基金 (61701067, 61771085, 61671095)
详细信息
    作者简介:

    罗忠涛:男,1984年生,讲师,硕士生导师,研究方向为统计信号处理与数字图像处理

    詹燕梅:女,1995年生,硕士生,研究方向为非高斯噪声信号处理理论与技术

    郭人铭:男,1995年生,硕士生,研究方向为大气噪声分析与低频通信技术

    张杨勇:男,1983生年,高级工程师,研究方向为低频通信技术与信号处理

    通讯作者:

    罗忠涛 luozt@cqupt.edu.cn

  • 中图分类号: TN911

Variable Tailing Nonlinear Transformation Design Based on Exponential Function in Impulsive Noise

Funds: The National Natural Science Foundation of China (61701067, 61771085, 61671095)
  • 摘要:

    针对脉冲噪声中的信号检测问题,该文提出一种基于指数函数的非线性变换函数设计与优化方法。该方法利用指数函数衰减速度可调的优点,适用于脉冲噪声的各种分布模型。通过引入效能函数,将非线性函数设计问题转化为以效能最大化为目标的阈值与底数参数优化问题。由于效能是关于待优化参数的连续可导且单峰函数,该优化问题可采用数值优化方法如单纯形法快速稳健地求解。性能分析表明,针对脉冲噪声常用的对称α稳定分布、Class A分布和高斯混合分布,该文方法均能取得基本最优检测性能,基于实测大气噪声仿真的通信误码率也明显优于传统的削波器和置零器。因此,该文为各种分布的脉冲噪声提供了一个统一的最优抑制解决方法。

  • 图  1  非线性变换函数的3种模式示意图

    图  2  ${\rm{S}}\alpha {\rm{S}}$噪声下的$E\left( {{g_X},T,a} \right)$曲面与曲线,$\alpha $=1.5, $\sigma $=1

    图  3  ${\rm{S}}\alpha {\rm{S}}$噪声中非线性函数比较

    图  4  ${\rm{S}}\alpha {\rm{S}}$噪声中非线性函数效能

    图  5  Class A噪声中非线性函数效能

    图  6  ${\rm{S}}\alpha {\rm{S}}$噪声$\alpha $变化时的最优参数

    图  7  通信误码率与信噪比的关系

    表  1  高斯混合噪声中非线性变换的效能

    $(\varepsilon ,\sigma _2^2)$=(0.3 10)(0.3 100)(0.3 1000)(0.1 10)(0.1 100)(0.1 1000)(0.01 10)(0.01 100)(0.01 1000)
    局部最优检测 0.5198 0.5709 0.6338 0.7935 0.8316 0.8678 0.9695 0.9796 0.9846
    最优置零器 0.4637 0.5421 0.6196 0.7624 0.8160 0.8611 0.9647 0.9752 0.9837
    最优削波器 0.4592 0.3906 0.3662 0.7407 0.6958 0.6793 0.9568 0.9453 0.9409
    GZMNL 0.5056 0.5674 0.6328 0.7883 0.8300 0.8672 0.9689 0.9774 0.9846
    GGM 0.4540 0.4982 0.5791 0.7576 0.7924 0.8311 0.9620 0.9691 0.9773
    X 轴平移模式 0.5079 0.5652 0.6313 0.7880 0.8286 0.8665 0.9686 0.9772 0.9845
    Y 轴平移模式 0.4939 0.5044 0.5512 0.7626 0.7614 0.7858 0.9599 0.9557 0.9589
    定点平移模式 0.5091 0.5282 0.5697 0.7776 0.7837 0.8032 0.9636 0.9618 0.9641
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-06-05
  • 修回日期:  2019-12-09
  • 网络出版日期:  2019-12-23
  • 刊出日期:  2020-06-04

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