摘要
在对炮膛进行检测时,由于温度、光照强度等影响,使得测得的信号带有很大的噪声,当噪声频带很宽时,自适应滤波器的参数设置比较困难,致使去噪效果不明显。为此,提出了一种基于经验模态分解的自适应滤波算法,该算法基于信号和噪声经过经验模态分解后在不同的IMF上有不同的特征,即先对信号进行经验模态分解,然后对各个高频IMF信号分别选用不同的滤波参数,进行自适应滤波处理。通过实验对比研究了该算法与普通自适应去噪、多尺度EMD滤波的降噪效果,实验表明,该算法具有很好的去噪效果。将该算法应用于炮膛检测系统中身管内径测量信号的降噪处理,取得了满意的效果。
When bore is being detected there are great noises in the measured signal due to the influence of temperature and in- tensity of illumination, and it is difficult to set the parameters of the adaptive filter for the wide frequency of noise, certainly, thus resul- ting bad denoising effect. Adaptive filter based on empirical mode decomposition, which is based on different characteristics of signal with noise in different IMFs, is introduced. At first empirical mode decomposition is used to divide signal and processed high-frequency IMF signals separately by adaptive filter. The denoising effect of the proposed method,usual filter and muhiscale EMD filter was investi- gated by experiment. It is shown that better that denoising effect for gun barrel radius measuring signals in detecting system of gun bore are achieved by using the proposed method.
出处
《信号处理》
CSCD
北大核心
2009年第6期958-962,共5页
Journal of Signal Processing
基金
国家自然科学基金(503175157)资助项目
关键词
经验模态分解
自适应滤波
炮膛检测
去噪
empirical mode decomposition
adaptive filter
gun bore detection
denoising
