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兵工学报 ›› 2017, Vol. 38 ›› Issue (7): 1416-1421.doi: 10.3969/j.issn.1000-1093.2017.07.021

• 论文 • 上一篇    下一篇

形态滤波方法在抵肩力信号降噪中的应用研究

宫鹏涵1,2, 周克栋1, 赫雷1, 陆野1   

  1. (1.南京理工大学 机械工程学院, 江苏 南京 210094; 2.军械工程学院 1系, 河北 石家庄 050003)
  • 收稿日期:2017-03-15 修回日期:2017-03-15 上线日期:2018-04-12
  • 通讯作者: 周克栋(1964—),男,教授,博士生导师 E-mail:zkd81151@njust.edu.cn
  • 作者简介:宫鹏涵(1981—),男,讲师,博士研究生。E-mail:gongpenghan@126.com

Application of Morphology Filtering Method in the De-noising of Firearms' Shoulder-force Signals

GONG Peng-han1,2, ZHOU Ke-dong1, HE Lei1, LU Ye1   

  1. (1.School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu, China;2.The First Department, Ordnance Engineering College, Shijiazhuang 050003, Hebei, China)
  • Received:2017-03-15 Revised:2017-03-15 Online:2018-04-12

摘要: 针对抵肩力实验信号采集过程中引入的噪声污染问题,基于数学形态学理论,提出了一种 自适应多尺度形态学降噪方法。该方法采用形态开闭-闭开运算提取含噪信号的正、负冲击成分,根据不同尺度形态运算后的噪声统计分布,对不同尺度下形态运算的结果进行加权平均去噪,解决了细节保持与噪声滤波之间的矛盾。仿真实验以高斯白噪声干扰下的blocks信号为研究对象,信噪比和均方根误差为评定降噪结果的标准,结果表明该方法与传统阈值降噪方法相比具有更好的降噪效果。抵肩力实验结果的应用表明,该方法能够在有效抑制信号噪声的同时较好地保留抵肩力信号特征细节,为抵肩力信号特征的提取提供了一种降噪方法。

关键词: 兵器科学与技术, 抵肩力信号, 数学形态学, 形态滤波, 自适应多尺度, 降噪

Abstract: A novel morphology de-noising method, i.e., adaptive multi-scale morphological analysis algorithm, is proposed based on mathematical morphology for the noise interference introduced in the acquisition process of the firearms' shoulder-force signals. The open-closing and close-opening operations are used to extract the positive and negative impacts from noisy signals. According to the statistical distribution of noises, the results of morphological analysis for different scales are weighted and averaged, and the contradiction between detail preserving and noise filtering is resolved. Blocks signal in Gaussian white noises is studied through simulation, and the de-noising performance of the proposed method is measured by examining the signal noise ratio and mean square error. The results show that the proposed method is able effectively to suppress Gaussian white noises while preserving the features of blocks signal. Key

Key words: ordnancescienceandtechnology, shoulder-forcesignal, mathematicalmorphology, morphologyfiltering, adaptivemulti-scale, de-noising

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