Perceiving harmonic information (especially weak harmonic information) in time series has important scientific and engineering significance. Fourier spectrum and time-frequency spectrum are commonly used tools for per...Perceiving harmonic information (especially weak harmonic information) in time series has important scientific and engineering significance. Fourier spectrum and time-frequency spectrum are commonly used tools for perceiving harmonic information, but they are often ineffective in perceiving weak harmonic signals because they are based on energy or amplitude analysis. Based on the theory of Normal time-frequency transform (NTFT) and complex correlation coefficient, a new type of spectrum, the Harmonicity Spectrum (HS), is developed to perceive harmonic information in time series. HS is based on the degree of signal harmony rather than energy or amplitude analysis, and can therefore perceive very weak harmonic information in signals sensitively. Simulation examples show that HS can detect harmonic information that cannot be detected by Fourier spectrum or time-frequency spectrum. Acoustic data analysis shows that HS has better resolution than traditional LOFAR spectrum.展开更多
本文主要研究信号的归一化峰度及其在弱非线性系统辨识中的应用策略问题。首先简要介绍了几类常见的无记忆/有记忆非线性模型及其表示方法;给出了信号的归一化峰度定义及重要性质;在此基础上,分别针对非线性系统的记忆效应和非线性阶数...本文主要研究信号的归一化峰度及其在弱非线性系统辨识中的应用策略问题。首先简要介绍了几类常见的无记忆/有记忆非线性模型及其表示方法;给出了信号的归一化峰度定义及重要性质;在此基础上,分别针对非线性系统的记忆效应和非线性阶数对系统输出信号归一化峰度的影响进行了理论推导和仿真分析,揭示了该参数随系统特性的变化规律,表明归一化峰度具备精确辨识弱非线性系统的潜力。最后,针对SFDR(无杂散动态范围)高达85dBFS(dB Full Scale)的弱非线性系统,本文提出了一种分步辨识的方法,并结合所提出的方法阐明了此规律对于弱非线性系统盲辨识和失真补偿的潜在应用价值及其精度优势。展开更多
文摘Perceiving harmonic information (especially weak harmonic information) in time series has important scientific and engineering significance. Fourier spectrum and time-frequency spectrum are commonly used tools for perceiving harmonic information, but they are often ineffective in perceiving weak harmonic signals because they are based on energy or amplitude analysis. Based on the theory of Normal time-frequency transform (NTFT) and complex correlation coefficient, a new type of spectrum, the Harmonicity Spectrum (HS), is developed to perceive harmonic information in time series. HS is based on the degree of signal harmony rather than energy or amplitude analysis, and can therefore perceive very weak harmonic information in signals sensitively. Simulation examples show that HS can detect harmonic information that cannot be detected by Fourier spectrum or time-frequency spectrum. Acoustic data analysis shows that HS has better resolution than traditional LOFAR spectrum.
文摘本文主要研究信号的归一化峰度及其在弱非线性系统辨识中的应用策略问题。首先简要介绍了几类常见的无记忆/有记忆非线性模型及其表示方法;给出了信号的归一化峰度定义及重要性质;在此基础上,分别针对非线性系统的记忆效应和非线性阶数对系统输出信号归一化峰度的影响进行了理论推导和仿真分析,揭示了该参数随系统特性的变化规律,表明归一化峰度具备精确辨识弱非线性系统的潜力。最后,针对SFDR(无杂散动态范围)高达85dBFS(dB Full Scale)的弱非线性系统,本文提出了一种分步辨识的方法,并结合所提出的方法阐明了此规律对于弱非线性系统盲辨识和失真补偿的潜在应用价值及其精度优势。
