Xing Yongchang, Hong Wei, Zhang Heng, Ding Youfeng, Sun Bin, Hu Wankun
{"title":"Parameter Estimation of Sinusoid Frequency Modulation Signal in Heavy-tailed Noise","authors":"Xing Yongchang, Hong Wei, Zhang Heng, Ding Youfeng, Sun Bin, Hu Wankun","doi":"10.1109/CTISC49998.2020.00014","DOIUrl":null,"url":null,"abstract":"In this paper, we proposed a novel algorithm based on the generalized cyclic stationary characteristics for estimating sinusoidal frequency-modulated (SFM) signals in the presence of heavy-tailed noise. The properties of the cyclic autocorrelation function for parameter estimation are first investigated. Then, the modulation frequency of the SFM signal is estimated based on the generalized cyclic stationary characteristics. Finally, the carrier frequency and modulation index are achieved by constructing the reference signal. Theoretical analysis and numerical simulation indicate that the proposed method can significantly improve the performance of parameters estimation of SFM signals in the presence of heavy-tailed noise.","PeriodicalId":266384,"journal":{"name":"2020 2nd International Conference on Advances in Computer Technology, Information Science and Communications (CTISC)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 2nd International Conference on Advances in Computer Technology, Information Science and Communications (CTISC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CTISC49998.2020.00014","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
In this paper, we proposed a novel algorithm based on the generalized cyclic stationary characteristics for estimating sinusoidal frequency-modulated (SFM) signals in the presence of heavy-tailed noise. The properties of the cyclic autocorrelation function for parameter estimation are first investigated. Then, the modulation frequency of the SFM signal is estimated based on the generalized cyclic stationary characteristics. Finally, the carrier frequency and modulation index are achieved by constructing the reference signal. Theoretical analysis and numerical simulation indicate that the proposed method can significantly improve the performance of parameters estimation of SFM signals in the presence of heavy-tailed noise.