摘要
本文提出了分形信号的小波分解与重构的一种快速算法.针对分形信号的自相似和长时相关的特点,采取离散小波变换(DWT)对分形信号进行多尺度分解,使其成为各尺度上的近似平稳信号,从而可利用通常的Wiener滤波[5]或Kalman滤波[7]方法进行估计,然后再由DWT进行多尺度重构,估计出被噪声污染了的原始信号.本文重点对分形信号的DWT进行算法设计,并估计了计算复杂度.
In this paper, a fast algorithm for the fractal signal wavelet decomposition and reconstruction is put forward. In accordance with the self similar and long-term related characteristics of the fractal signals, and by means of discrete wavelet transformation (DWT), multi-scale decomposition is carried out so as to make them become similar stationary signals and estimate them with the usual wiener filtering of Kalman filtering method. Then multi-scale reconstruction is carried out with DWT in order to estimate the primary signals polluted by noises. This paper stresses the algorithm design of the DWT for the fractal signals, and the computing complexity is also considered.
出处
《工程图学学报》
CSCD
1999年第2期27-34,共8页
Journal of Engineering Graphics