计算机应用 ›› 2011, Vol. 31 ›› Issue (10): 2790-2792.DOI: 10.3724/SP.J.1087.2011.02790

• 人工智能 • 上一篇    下一篇

基于平稳小波变换的飞行数据去噪方法

李正欣1,张凤鸣1,张晓丰1,费文2   

  1. 1.空军工程大学 工程学院, 西安 710038
    2.北京军区 空军装备部,北京 100061
  • 收稿日期:2011-04-25 修回日期:2011-06-13 发布日期:2011-10-11 出版日期:2011-10-01
  • 通讯作者: 李正欣
  • 作者简介:李正欣(1982-),男,河南信阳人,博士研究生,主要研究方向:信息系统工程、智能决策、数据挖掘;张凤鸣(1963-),男,重庆人,教授,博士生导师,主要研究方向:信息系统工程、智能决策;张晓丰(1978-),男,天津人,讲师,博士,主要研究方向:信息系统工程、智能决策;费文(1981-),男,黑龙江大庆人,硕士,主要研究方向:装备管理与决策。

Flight data denoising method based on stationary wavelet transform

LI Zheng-xin1, ZHANG Feng-ming1, ZHANG Xiao-feng1, FEI Wen2   

  1. 1.Engineering Institute, Air force Engineering University, Xi'an Shaanxi 710038, China
    2.Air Force Armament Department, Beijing Military Area Command, Beijing 100061, China
  • Received:2011-04-25 Revised:2011-06-13 Online:2011-10-11 Published:2011-10-01

摘要: 为更有效地去除飞行数据中的噪声,分析了平稳小波变换的基本原理,将小波系数相关性与阈值收缩去噪方法相结合,提出一种基于系数相关性的改进阈值函数去噪方法。该方法采用平稳小波变换,先对小波系数进行相关性分析,而后使用改进的阈值函数对小波系数进行阈值处理,最后进行信号重构。实验结果表明:该方法不仅能够很好地保持信号的形状,而且信噪比较高、均方误差较小;在实际的飞行数据处理中能够获得较好的去噪效果。

关键词: 飞行数据, 多元时间序列, 平稳小波变换, 信号去噪, 阈值函数

Abstract: In order to get rid of the noise of flight data more effectively, based on discussing the principle of Stationary Wavelet Transform (SWT), a new denoising method was proposed, which combined correlation of wavelet coefficient with wavelet shrinkage. Firstly, signals were decomposed by using SWT; secondly, wavelet coefficient was dealt with by using methods of coefficient correlation and wavelet shrinkage in sequence; at last, denoised signal was reconstructed through inverse wavelet transform. The results of experiments show that the proposed method can raise Signal-to-Noise Ratio (SNR), decrease Mean Squared Error (MSE) and preserve the shape of signal; and it can be applied to flight data effectively.

Key words: flight data, multivariate time series, Stationary Wavelet Transform (SWT), signal denoising, threshold function

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