Channel Noise Suppression Method Based on Median Filtering Using Local Variance for Extreme/Super Low Frequency Communications

ZHAO Peng; JIANG Yu-zhong; ZHAI Qi; XIANG Bing; WANG Xi-chen

doi:10.3969/j.issn.0372-2112.2019.04.023

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Channel Noise Suppression Method Based on Median Filtering Using Local Variance for Extreme/Super Low Frequency Communications

更新时间：2025-07-16

- Channel Noise Suppression Method Based on Median Filtering Using Local Variance for Extreme/Super Low Frequency Communications
- Acta Electronica Sinica Vol. 47, Issue 4, Pages: 955-961(2019)
- 作者机构：
  
  1. 海军工程大学电子工程学院,湖北,武汉,430033
  2. 中船重工集团公司第七二二研究所,湖北,武汉,430079
  3. 海军工程大学电子工程学院,湖北,武汉,430033
  4. 中船重工集团公司第七二二研究所,湖北,武汉,430079
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2019.04.023
  CLC： TN850.8
- Published Online：25 April 2019，
  
  Published：2019
- 稿件说明：
移动端阅览
ZHAO Peng, JIANG Yu-zhong, ZHAI Qi, et al. Channel Noise Suppression Method Based on Median Filtering Using Local Variance for Extreme/Super Low Frequency Communications[J]. Acta Electronica Sinica, 2019, 47(4): 955-961.
DOI：

ZHAO Peng, JIANG Yu-zhong, ZHAI Qi, et al. Channel Noise Suppression Method Based on Median Filtering Using Local Variance for Extreme/Super Low Frequency Communications[J]. Acta Electronica Sinica, 2019, 47(4): 955-961. DOI： 10.3969/j.issn.0372-2112.2019.04.023.

摘要

针对极/超低频信道噪声模型参数估计复杂度高而难以实施最优非线性处理的问题，根据噪声时域波形分析，本文提出了一种基于局部方差中值滤波的抑噪方法.该方法利用局部方差提取结构信息以强化噪声脉冲性，提高中值滤波抑噪性能；采用基于恒虚警率的奇异值检测方法实现含噪样点的盲判决，利用自适应中值滤波算法实现噪声的盲抑制.仿真和实测结果验证了该方法的有效性.相比最优非线性处理，该方法无需噪声参数估计，因而更具实用性.

Abstract

Aiming at the problem that parameter estimation for extremely/super low frequency channel noise model is so complicated that it is difficult to perform optimal nonlinear processing and according to the time-domain analysis of the noise waveforms

a noise suppression method was proposed based on median filtering (MF) across local variance.It utilized the local variance to extract the structural information so that the noise impulsiveness would be enhanced and the performance of MF would be improved;utilized the constant false alarm rate outlier detection method to achieve the blind determination of the noisy samples and the adaptive MF algorithm to achieve the blind suppressions.Simulations and real tests verified its effectiveness.Due to no parameter estimation

this method is more practical than the optimal nonlinearities.

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