Q. Wan, Ya Wang, Xinxia Feng, L. Zou, Xiansheng Guo
{"title":"Lecture Notes on Eigen-analysis of Autocorrelation and Power Spectrum Density Function","authors":"Q. Wan, Ya Wang, Xinxia Feng, L. Zou, Xiansheng Guo","doi":"10.12783/DTEM/EEIM2020/35177","DOIUrl":null,"url":null,"abstract":"Eigen-analysis is a quite important tool in signal processing, from which we can analyze the random signal. In this paper, the eigen-analysis of the autocorrelation function and power spectrum density are analyzed using Einstein-Wiener-khintchine relationship, LTI system, AR model, etc. The relationship between the two is illustrated. The eigen-analysis of the autocorrelation function of the random signal and power spectrum density can be derived from each other. We hope that the results of this lecture notes can give some inspiration to students who are studying digital signal processing.","PeriodicalId":285319,"journal":{"name":"DEStech Transactions on Economics, Business and Management","volume":"41 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"DEStech Transactions on Economics, Business and Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.12783/DTEM/EEIM2020/35177","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Eigen-analysis is a quite important tool in signal processing, from which we can analyze the random signal. In this paper, the eigen-analysis of the autocorrelation function and power spectrum density are analyzed using Einstein-Wiener-khintchine relationship, LTI system, AR model, etc. The relationship between the two is illustrated. The eigen-analysis of the autocorrelation function of the random signal and power spectrum density can be derived from each other. We hope that the results of this lecture notes can give some inspiration to students who are studying digital signal processing.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
