论文标题

自适应短时傅立叶转换基于线性chirp局部近似的自适应短期傅立叶转换信号分离方法

Analysis of an Adaptive Short-Time Fourier Transform-Based Multicomponent Signal Separation Method Derived from Linear Chirp Local Approximation

论文作者

Chui, Charles K., Jiang, Qingtang, Li, Lin, Lu, Jian

论文摘要

Synchrosqueezing Transform(SST)已被开发为一种强大的类似EMD的工具,用于瞬时频率(IF)估计和非平稳多组分信号的组件分离。最近,引入了一种直接的时频方法,称为信号分离操作(SSO),以解决多组分信号分离的问题。虽然SST和SSO在数学上都在估计上都很严格,但SSO避免了组件恢复中的两步SST方法的第二步(模式检索)。另外,SSO很简单:IF组件的IF是通过SSO平面的时频脊来估计的;只需将时间频率插入SSO操作,就可以恢复此组件。在最近的论文“通过提取具有自适应时间变化参数的局部频率的直接信号分离”,在表明SSO操作与自适应短时短时傅立叶变换(STFT)相关之后,作者是从线性chirp(也称为线性调制信号)恢复的一个更准确的组件恢复公式(也称为一个均一的率)。为每个组件更新时变窗口。但是,尚未研究从线性chirp局部近似衍生的恢复公式的理论分析。在本文中,我们进行了此类分析,并获得误差范围,以进行IF估计和组件恢复。这些结果为提出的基于自适应STFT的非平稳多组分信号分离方法提供了数学保证。

The synchrosqueezing transform (SST) has been developed as a powerful EMD-like tool for instantaneous frequency (IF) estimation and component separation of non-stationary multicomponent signals. Recently, a direct method of the time-frequency approach, called signal separation operation (SSO), was introduced to solving the problem of multicomponent signal separation. While both SST and SSO are mathematically rigorous on IF estimation, SSO avoids the second step of the two-step SST method in component recovery (mode retrieval). In addition, SSO is simple: the IF of a component is estimated by a time-frequency ridge of the SSO plane; and this component is recovered by simply plugging the time-frequency ridge to the SSO operation. In recent paper "Direct signal separation via extraction of local frequencies with adaptive time-varying parameters", after showing that the SSO operation is related to the adaptive short-time Fourier transform (STFT), the authors obtained a more accurate component recovery formula derived from the linear chirp (also called linear frequency modulation signal) approximation at any local time and they also proposed a recovery scheme to extract the signal components one by one with the time-varying window updated for each component. However the theoretical analysis of the recovery formula derived from linear chirp local approximation has not been studied there. In this paper, we carry out such analysis and obtain error bounds for IF estimation and component recovery. These results provide a mathematical guarantee to the proposed adaptive STFT-based non-stationary multicomponent signal separation method.

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