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首页> 外文期刊>Advances in Adaptive Data Analysis >NOISE CORRUPTION OF EMPIRICAL MODE DECOMPOSITION AND ITS EFFECT ON INSTANTANEOUS FREQUENCY
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NOISE CORRUPTION OF EMPIRICAL MODE DECOMPOSITION AND ITS EFFECT ON INSTANTANEOUS FREQUENCY

机译：经验模态分解的噪声校正及其对瞬时频率的影响

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Huang's Empirical Mode Decomposition (EMD) is an algorithm for analyzing nonstationary data that provides a localized time-frequency representation by decomposing the data into adaptively defined modes. EMD can be used to estimate a signal's instantaneous frequency (IF) but suffers from poor performance in the presence of noise. To produce a meaningful IF, each mode of the decomposition must be nearly monochromatic, a condition that is not guaranteed by the algorithm and fails to be met when the signal is corrupted by noise. In this work, the extraction of modes containing both signal and noise is identified as the cause of poor IF estimation. The specific mechanism by which such "transition" modes are extracted is detailed and builds on the observation of Flandrin and Goncalves that EMD acts in a filter bank manner when analyzing pure noise. The mechanism is shown to be dependent on spectral leak between modes and the phase of the underlying signal. These ideas are developed through the use of simple signals and are tested on a synthetic seismic waveform.
机译：Huang的经验模式分解（EMD）是一种用于分析非平稳数据的算法，该算法通过将数据分解为自适应定义的模式来提供本地化的时频表示。 EMD可以用于估计信号的瞬时频率（IF），但在存在噪声的情况下性能较差。为了产生有意义的IF，分解的每种模式都必须接近单色，该条件不能由算法保证，并且在信号被噪声破坏时无法满足。在这项工作中，包含信号和噪声的模式提取被确定为中频估计不佳的原因。详细介绍了提取此类“过渡”模式的具体机制，并且建立在对Flandrin和Goncalves的观察的基础上，EMD在分析纯噪声时以滤光片方式起作用。示出该机制取决于模式之间的频谱泄漏和基础信号的相位。这些想法是通过使用简单的信号开发出来的，并在合成地震波形上进行了测试。

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