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信号与信息处理是信息科学中近十几年来发展最为迅速的学科之一。国内外学术界都将注意力转向到非平稳信号的分析与处理,其中一些分支,如信号的时频表示法(包括小波变换、维格纳分布等)、时变参数信号模型法已成为当前国内外学术界研究热点之一。本文集中讨论了传统的Fourier分析,近年来新兴的而且发展比较成熟的小波分析方法,以及最近提出的新的方法—经验模式分解(Empirical Mode Decomposition:EMD)。
Signal and information processing is one of the fastest growing disciplines in information science in recent decades. At home and abroad, academic circles turn their attention to the analysis and processing of non-stationary signals. Some of them, such as the time-frequency representation of signals (including wavelet transform and Wigner distribution), the time-varying parametric signal modeling method has become the current One of the hot topics in academic circles at home and abroad. This article focuses on the traditional Fourier analysis, the emerging and more mature wavelet analysis methods in recent years, and the recently proposed Empirical Mode Decomposition (EMD).