Digital Spectral Analysis by means of the Method of Averag Modified Periodograms Using Binary-Sign Stochastic Quantization of Signals

IF 0.2 Q4 INSTRUMENTS & INSTRUMENTATION
V. Yakimov
{"title":"Digital Spectral Analysis by means of the Method of Averag Modified Periodograms Using Binary-Sign Stochastic Quantization of Signals","authors":"V. Yakimov","doi":"10.21122/2220-9506-2021-12-3-220-221","DOIUrl":null,"url":null,"abstract":"The method of averaging modified periodograms is one of the main methods for estimating the power spectral density (PSD). The aim of this work was the development of mathematical and algorithmic support, which can increase the computational efficiency of signals digital spectral analysis by this method.The solution to this problem is based on the use of binary-sign stochastic quantization for converting the analyzed signal into a digital code. A special feature of this quantization is the use of a randomizing uniformly distributed auxiliary signal as a stochastic continuous quantization threshold (threshold function). Taking into account the theory of discrete-event modeling the result of binary-sign quantization is interpreted as a chronological sequence of instantaneous events in which its values change. In accordance with this we have a set of time samples that uniquely determine the result of binary-sign quantization in discrete-time form. Discrete-event modeling made it possible to discretize the process of calculating PSD estimates. As a result, the calculation of PSD estimates was reduced to discrete processing of the cosine and sine Fourier transforms for window functions. These Fourier transforms are calculated analytically based on the applied window functions. The obtained mathematical equations for calculating the PSD estimates practically do not require multiplication operations. The main operations of these equations are addition and subtraction. As a consequence, the time spent on digital spectral analysis of signals is reduced.Numerical experiments have shown that the developed mathematical and algorithmic support allows us to calculate the PSD estimates by the method of averaging modified periodograms with a high frequency resolution and accuracy even for a sufficiently low signal-to-noise ratio. This result is especially important for spectral analysis of broadband signals.The developed software module is a problem-oriented component that can be used as part of metrologically significant software for the operational analysis of complex signals.","PeriodicalId":41798,"journal":{"name":"Devices and Methods of Measurements","volume":null,"pages":null},"PeriodicalIF":0.2000,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Devices and Methods of Measurements","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21122/2220-9506-2021-12-3-220-221","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"INSTRUMENTS & INSTRUMENTATION","Score":null,"Total":0}
引用次数: 0

Abstract

The method of averaging modified periodograms is one of the main methods for estimating the power spectral density (PSD). The aim of this work was the development of mathematical and algorithmic support, which can increase the computational efficiency of signals digital spectral analysis by this method.The solution to this problem is based on the use of binary-sign stochastic quantization for converting the analyzed signal into a digital code. A special feature of this quantization is the use of a randomizing uniformly distributed auxiliary signal as a stochastic continuous quantization threshold (threshold function). Taking into account the theory of discrete-event modeling the result of binary-sign quantization is interpreted as a chronological sequence of instantaneous events in which its values change. In accordance with this we have a set of time samples that uniquely determine the result of binary-sign quantization in discrete-time form. Discrete-event modeling made it possible to discretize the process of calculating PSD estimates. As a result, the calculation of PSD estimates was reduced to discrete processing of the cosine and sine Fourier transforms for window functions. These Fourier transforms are calculated analytically based on the applied window functions. The obtained mathematical equations for calculating the PSD estimates practically do not require multiplication operations. The main operations of these equations are addition and subtraction. As a consequence, the time spent on digital spectral analysis of signals is reduced.Numerical experiments have shown that the developed mathematical and algorithmic support allows us to calculate the PSD estimates by the method of averaging modified periodograms with a high frequency resolution and accuracy even for a sufficiently low signal-to-noise ratio. This result is especially important for spectral analysis of broadband signals.The developed software module is a problem-oriented component that can be used as part of metrologically significant software for the operational analysis of complex signals.
基于信号二符号随机量化的平均修正周期图方法的数字频谱分析
修正周期图平均法是估计功率谱密度的主要方法之一。本工作的目的是开发数学和算法支持,从而提高用该方法进行信号数字频谱分析的计算效率。该问题的解决方案是基于使用二进制符号随机量化将分析信号转换为数字代码。这种量化的一个特殊特点是使用随机均匀分布的辅助信号作为随机连续量化阈值(阈值函数)。考虑到离散事件建模理论,二进制符号量化的结果被解释为瞬时事件的时间顺序,其中其值发生变化。根据这一点,我们有一组时间样本,唯一地确定离散时间形式的二进制符号量化结果。离散事件建模使计算PSD估计的过程离散化成为可能。因此,PSD估计的计算被简化为窗函数的余弦和正弦傅里叶变换的离散处理。这些傅里叶变换是基于应用的窗函数解析计算的。所得的计算PSD估计的数学方程实际上不需要乘法运算。这些方程的主要运算是加法和减法。因此,减少了用于信号数字频谱分析的时间。数值实验表明,所开发的数学和算法支持使我们能够通过平均修正周期图的方法计算PSD估计,即使在足够低的信噪比下也具有很高的频率分辨率和精度。这一结果对宽带信号的频谱分析尤为重要。所开发的软件模块是一个面向问题的组件,可以作为计量意义软件的一部分,用于复杂信号的操作分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Devices and Methods of Measurements
Devices and Methods of Measurements INSTRUMENTS & INSTRUMENTATION-
自引率
25.00%
发文量
18
审稿时长
8 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信