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

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