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Consecutive minimum phase expansion of physically realizable signals with applications

机译:物理可实现信号与应用程序的连续最小相位扩展

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In digital signal processing, it is a well know fact that a causal signal of finite energy is front loaded if and only if the corresponding analytic signal, or the physically realizable signal, is a minimum phase signal, or an outer function in the complex analysis terminology. Based on this fact, a series expansion method, called unwinding adaptive Fourier decomposition (AFD), to give rise to positive frequency representations with rapid convergence was proposed several years ago. It appears to be a promising positive frequency representation with great potential of applications. The corresponding algorithm, however, is complicated due to consecutive extractions of outer functions involving computation of Hilbert transforms. This paper is to propose a practical algorithm for unwinding AFD that does not depend on computation of Hilbert transform, but, instead, factorizes out the Blaschke product type of inner functions. The proposed method significantly improves applicability of unwinding AFD. As an application, we give the associated Dirac-type time-frequency distribution of physically realizable signals. Copyright (c) 2015 John Wiley & Sons, Ltd.
机译:在数字信号处理中,众所周知的事实是,当且仅当相应的分析信号或可物理实现的信号是最小相位信号或复杂分析中的外部函数时,才会预先加载有限能量的因果信号术语。基于这一事实,几年前提出了一种称为展开式傅立叶分解(AFD)的级数展开方法,以快速收敛产生正频率表示。它似乎是一个很有前途的正频率表示形式,具有巨大的应用潜力。然而,由于涉及希尔伯特变换的计算的外部函数的连续提取,相应的算法很复杂。本文旨在提出一种不依赖于希尔伯特变换计算的实用AFD展开算法,而是分解出内部函数的Blaschke乘积类型。所提出的方法大大提高了放卷AFD的适用性。作为应用,我们给出了物理可实现信号的相关狄拉克型时频分布。版权所有(c)2015 John Wiley&Sons,Ltd.

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