{"title":"一种新的正弦信号频率估计自相关方法","authors":"Chao H. Huang, Jidong Suo, T. Liu","doi":"10.1109/CCSSE.2016.7784355","DOIUrl":null,"url":null,"abstract":"Based on the linear prediction (LP) property of sinusoidal signals, a novel autocorrelation approach for a single-tone sinusoidal signal emerged in Gaussian white noise is proposed. Firstly, a new closed-form autocorrelation expression is derived that it contributes more to frequency estimation than conventional autocorrelation expressions, then, inspired by RIM estimator, we propose a novel estimator using multiple autocorrelation lags. Computer simulations show that the accuracy of the proposed approach is closer to the Cramer-Rao bound(CRB) especially for lower signal noise radio(SNR) and has lower computation complexity via comparing with several autocorrelation-based estimators.","PeriodicalId":136809,"journal":{"name":"2016 2nd International Conference on Control Science and Systems Engineering (ICCSSE)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A novel autocorrelation approach for frequency estimation of a sinusoid\",\"authors\":\"Chao H. Huang, Jidong Suo, T. Liu\",\"doi\":\"10.1109/CCSSE.2016.7784355\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Based on the linear prediction (LP) property of sinusoidal signals, a novel autocorrelation approach for a single-tone sinusoidal signal emerged in Gaussian white noise is proposed. Firstly, a new closed-form autocorrelation expression is derived that it contributes more to frequency estimation than conventional autocorrelation expressions, then, inspired by RIM estimator, we propose a novel estimator using multiple autocorrelation lags. Computer simulations show that the accuracy of the proposed approach is closer to the Cramer-Rao bound(CRB) especially for lower signal noise radio(SNR) and has lower computation complexity via comparing with several autocorrelation-based estimators.\",\"PeriodicalId\":136809,\"journal\":{\"name\":\"2016 2nd International Conference on Control Science and Systems Engineering (ICCSSE)\",\"volume\":\"42 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 2nd International Conference on Control Science and Systems Engineering (ICCSSE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CCSSE.2016.7784355\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 2nd International Conference on Control Science and Systems Engineering (ICCSSE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCSSE.2016.7784355","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A novel autocorrelation approach for frequency estimation of a sinusoid
Based on the linear prediction (LP) property of sinusoidal signals, a novel autocorrelation approach for a single-tone sinusoidal signal emerged in Gaussian white noise is proposed. Firstly, a new closed-form autocorrelation expression is derived that it contributes more to frequency estimation than conventional autocorrelation expressions, then, inspired by RIM estimator, we propose a novel estimator using multiple autocorrelation lags. Computer simulations show that the accuracy of the proposed approach is closer to the Cramer-Rao bound(CRB) especially for lower signal noise radio(SNR) and has lower computation complexity via comparing with several autocorrelation-based estimators.