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Refining the ambiguity domain characteristics of non-stationary signals for improved time-frequency analysis: Test case of multidirectional and multicomponent piecewise LFM and HFM signals

机译:改进非静止信号的模糊域特性,以改进的时频分析:多向和多组分分段LFM和HFM信号的测试用例

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This paper aims at providing a more accurate description of the ambiguity domain characteristics of a piecewise multicomponent non-stationary signals with focus on piece-wise linear frequency modulated (LFM) (PW-LFM) signal and a mixed LFM and hyperbolic FM (HFM). The main motivation comes from the observed PW-LFM nature of several real life signals. It is essential that the characteristics of these types of signals be taken into account for the design of high resolution Time-Frequency Distributions (TFDs) and therefore to improve the application and interpretation of time-frequency signal analysis and processing. In this paper the ambiguity function (AF) of a general PW LFM signal is derived exactly and then analyzed to deduce important properties. The precise location and behavior of both auto-terms and cross-terms of a general piecewise LFM signal can be deduced from its AF. Numerical simulations using different types of test signals confirm the analytical derivations. An extension of the PW-LFM test signal is also presented by using HFM signals. For such signals, the exact analytical expression of auto-terms is given as well as the expression of cross-terms between HFM and LFM. The results obtained can be used in future studies to design more advanced quadratic time-frequency distributions (QTFDs) that exhibit improved properties in terms of resolution and accuracy. (C) 2018 Elsevier Inc. All rights reserved.
机译:本文旨在提供一种更准确地描述分段多组分非静止信号的歧义域特性,其专注于分段线性频率调制(LFM)(PW-LFM)信号和混合的LFM和双曲线FM(HFM) 。主要动机来自几个真实寿命信号的观察到的PW-LFM性质。必须考虑到这些类型信号的特性,以考虑高分辨率时频分布(TFD)的设计,从而提高时间频率信号分析和处理的应用和解释。在本文中,一般PW LFM信号的模糊函数(AF)始于衍生,然后分析以推导出重要的属性。可以从其AF推导出一般分段LFM信号的自动术语和交叉级的精确位置和行为。使用不同类型的测试信号的数值模拟确认分析衍生。通过使用HFM信号还呈现PW-LFM测试信号的扩展。对于这样的信号,给出了自动术语的精确分析表达以及HFM和LFM之间的横向术语的表达。所获得的结果可用于未来的研究,以设计更先进的二次时频分布(QTFD),在分辨率和准确性方面表现出改进的性质。 (c)2018年Elsevier Inc.保留所有权利。

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