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Detecting the number of components in a non-stationary signal using the Rényi entropy of its time-frequency distributions

机译:利用其时频分布的Rényi熵检测非平稳信号中的分量数

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摘要

A time-frequency distribution provides many advantages in the analysis of multicomponent non-stationary signals. The simultaneous signal representation with respect to the time and frequency axis defines the signal amplitude, frequency, bandwidth, and the number of components at each time moment. The Rényi entropy, applied to a time-frequency distribution, is shown to be a valuable indicator of the signal complexity. The aim of this paper is to determine which of the treated time-frequency distributions (TFDs) (namely, the Wigner-Ville distribution, the Choi-Williams distribution, and the spectrogram) has the best properties for estimation of the number of components when there is no prior knowledge of the signal. The optimal Rényi entropy parameter α is determined for each TFD. Accordingly, the effects of different time durations, bandwidths and amplitudes of the signal components on the Rényi entropy have been analysed. The concept of a class, when the Rényi entropy is applied to TFDs, is also introduced.
机译:时频分布在多分量非平稳信号分析中提供了许多优势。关于时间和频率轴的同时信号表示定义了每个时间点的信号幅度,频率,带宽和分量数。应用于时频分布的Rényi熵被证明是信号复杂度的重要指标。本文的目的是确定哪种处理后的时频分布(TFD)(即Wigner-Ville分布,Choi-Williams分布和频谱图)具有最佳的属性来估计分量数。没有信号的先验知识。为每个TFD确定最佳Rényi熵参数α。因此,已经分析了不同持续时间,带宽和信号分量的幅度对Rényi熵的影响。还介绍了当将Rényi熵应用于TFD时的类的概念。

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