{"title":"Characteristic Function Based Parameter Estimation for Ocean Ambient Noise","authors":"Xuebo Zhang, Cheng Tan, Wenwei Ying","doi":"10.1109/ICIVC.2018.8492728","DOIUrl":null,"url":null,"abstract":"The parameter initializations play an important role in the iteration of parameter estimation. Based on characteristic function, a parameter estimation method for Class B noise considering the parameter initialization is presented in this paper. The noise is firstly considered as the symmetric alpha stable (SαS) distribution. With the log method, we get the estimated parameters, which are further used as the parameter initial values of iteration. It improves the convergence speed. The processing results of simulated data indicate that the parameters of Class B noise can be efficiently estimated with the presented method.","PeriodicalId":173981,"journal":{"name":"2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIVC.2018.8492728","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
The parameter initializations play an important role in the iteration of parameter estimation. Based on characteristic function, a parameter estimation method for Class B noise considering the parameter initialization is presented in this paper. The noise is firstly considered as the symmetric alpha stable (SαS) distribution. With the log method, we get the estimated parameters, which are further used as the parameter initial values of iteration. It improves the convergence speed. The processing results of simulated data indicate that the parameters of Class B noise can be efficiently estimated with the presented method.