Yonggang Zhang, Honggang Chen, Tao Li, Deshuang Wang
{"title":"Pre-estimate Relative Intensity Noise subtraction performance of FOG by using signal cross-correlation","authors":"Yonggang Zhang, Honggang Chen, Tao Li, Deshuang Wang","doi":"10.1109/ICINFA.2011.5949097","DOIUrl":null,"url":null,"abstract":"A method of pre-estimate Relative Intensity Noise (RIN) subtraction performance in Interference Fiber Optic Gyroscope (IFOG) is introduced. Experiment shows that the noise subtraction result depends on the cross-correlation coefficient between FOG's output and coupler's free port signal. The variance of the noise would rise when cross-correlation coefficient is lower then 0.5 and reduce to 17.16% with crosscorrelation coefficient 0.91. Estimating the cross-correlation coefficient before noise subtraction can avoid the situation that the noise increase after noise subtraction, and finally enhance the reliability of noise subtraction.","PeriodicalId":299418,"journal":{"name":"2011 IEEE International Conference on Information and Automation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 IEEE International Conference on Information and Automation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICINFA.2011.5949097","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
A method of pre-estimate Relative Intensity Noise (RIN) subtraction performance in Interference Fiber Optic Gyroscope (IFOG) is introduced. Experiment shows that the noise subtraction result depends on the cross-correlation coefficient between FOG's output and coupler's free port signal. The variance of the noise would rise when cross-correlation coefficient is lower then 0.5 and reduce to 17.16% with crosscorrelation coefficient 0.91. Estimating the cross-correlation coefficient before noise subtraction can avoid the situation that the noise increase after noise subtraction, and finally enhance the reliability of noise subtraction.