论文标题
基于合成的时间尺度转换非平稳信号
Synthesis-based time-scale transforms for non-stationary signals
论文作者
论文摘要
本文从信号综合的角度介绍了非平稳信号的建模。引入和研究了由随机时间尺度表示产生的一类随机，非平稳信号。非平稳性是通过先前的分布在时间尺度表示中实现的，该分布模拟了时间翘曲对固定信号的作用。该方法的主要原始性是模型直接构成可以合成信号的时间尺度表示，而不是后处理预计的时间尺度变换。根据期望最大化方法，提出了最大的后验估计器，并针对静置固定信号的时间扭曲参数和底层固定信号的功率谱以及称为JEFAS-S的迭代算法，称为JEFAS-S进行估计。数值结果表明JEFAS-S准确估算时间扭曲和功率谱的能力。当时间扭曲涉及快速变化时，这尤其是正确的，在此类似的方法称为Jefas（早些时候）失败的情况下。此外，作为副产品，该方法能够产生极其清晰的时间尺度表示形式，也可以在速度变化的非平稳性的情况下，在这种情况下，标准方法（例如同步性方法）失败。
This paper deals with the modeling of non-stationary signals, from the point of view of signal synthesis. A class of random, non-stationary signals, generated by synthesis from a random timescale representation, is introduced and studied. Non-stationarity is implemented in the timescale representation through a prior distribution which models the action of time warping on a stationary signal. A main originality of the approach is that models directly a timescale representation from which signals can be synthesized, instead of post-processing a pre-computed timescale transform. A maximum a posteriori estimator is proposed for the time warping parameters and the power spectrum of an underlying stationary signal, together with an iterative algorithm, called JEFAS-S, for the estimation, based upon the Expectation Maximization approach. Numerical results show the ability of JEFAS-S to estimate accurately time warping and power spectrum. This is in particular true when time warping involves fast variations, where a similar approach called JEFAS, proposed earlier, fails. In addition, as a by-product, the approach is able to yield extremely sharp timescale representations, also in the case of fast varying non-stationarity, where standard approaches such as synchrosqueezing fail.
