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抑制脉冲型噪声的限幅器自适应设计
引用本文:罗忠涛,卢鹏,张杨勇,张刚.抑制脉冲型噪声的限幅器自适应设计[J].电子与信息学报,2019,41(5):1160-1166.
作者姓名:罗忠涛  卢鹏  张杨勇  张刚
作者单位:重庆邮电大学通信与信息工程学院 重庆 400065;武汉船舶通信研究所 武汉 430079
基金项目:国家自然科学基金;国家自然科学基金;国家自然科学基金;重庆市教委科研项目;重庆市教委科研项目
摘    要:针对脉冲型噪声的抑制问题,该文提出一种自适应的限幅器设计方法。该方法以效能函数为指标,采用自适应搜索算法,自动寻找削波器和置零器的最佳门限,且能适用于未知噪声分布的情形。首先分析了效能与非线性函数的关系,给出关键的优化问题。然后考虑到效能函数计算复杂,提出基于线搜索的自适应设计算法。其次针对未知分布情况,考虑非参数化的概率密度估计,该算法能够稳健运行且基本取得最优设计效果。最后,结合两种非高斯噪声和实测大气噪声数据仿真,结果表明:该文方法可自适应寻找最佳门限,使削波器和置零器效能达到最佳;当噪声分布未知时,该文方法无需假设噪声模型,可与非参数化概率密度估计方法结合,取得最优检测效果。

关 键 词:非线性处理    效能函数    自适应优化    削波器    置零器
收稿时间:2018-06-22

Adaptive Design of Limiters for Impulsive Noise Suppression
Zhongtao LUO,Peng LU,Yangyong ZHANG,Gang ZHANG.Adaptive Design of Limiters for Impulsive Noise Suppression[J].Journal of Electronics & Information Technology,2019,41(5):1160-1166.
Authors:Zhongtao LUO  Peng LU  Yangyong ZHANG  Gang ZHANG
Affiliation:1.School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China2.Wuhan Maritime Communication Research Institute, Wuhan 430079, China
Abstract:An adaptive method of limiter design is proposed to suppress impulsive noise. With a purpose of maximizing the efficacy function, the proposed method searches for optimal thresholds of clipper and blanker, via adaptive line search. Firstly, based on analysis on the relationship between the efficacy and the nonlinearity, the key problem of optimization is proposed. Then, since the calculation of efficacy is hard, an adaptive algorithm based on linear search approach is developed based on linear search to optimize the efficacy. Considering the noise distribution is unknown, the proposed method employs the nonparametric kernel density estimation and works robustly in the presence of estimation error. Finally, numeric simulations demonstrate that the proposed method can obtain the optimal performance of clippers and blankers successfully. In the processing of real atmospheric noise from unknown distribution, the proposed method achieves the best detection performance when combining nonparametric kernel density estimation approach.
Keywords:
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