{"title":"基于变分贝叶斯方法的k分布形状参数估计","authors":"A. Turlapaty","doi":"10.1109/SSP.2018.8450847","DOIUrl":null,"url":null,"abstract":"The sea clutter component in some of the radar and sonar signal models can be statistically characterized as following a K-distribution. This distribution has a shape parameter that is directly related to the number of scatterers. Hence, the estimation of this shape parameter is an important problem and is traditionally addressed using the maximum likelihood (ML), the method of moments (MoM) and their variants. A shortcoming of these methods is lesser accuracy in comparison to the theoretical CRB. In this paper, a variational Bayesian algorithm is proposed that provides both improved convergence and superior accuracy in comparison to the existing algorithms.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Shape Parameter Estimation for K-Distribution Using Variational Bayesian Approach\",\"authors\":\"A. Turlapaty\",\"doi\":\"10.1109/SSP.2018.8450847\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The sea clutter component in some of the radar and sonar signal models can be statistically characterized as following a K-distribution. This distribution has a shape parameter that is directly related to the number of scatterers. Hence, the estimation of this shape parameter is an important problem and is traditionally addressed using the maximum likelihood (ML), the method of moments (MoM) and their variants. A shortcoming of these methods is lesser accuracy in comparison to the theoretical CRB. In this paper, a variational Bayesian algorithm is proposed that provides both improved convergence and superior accuracy in comparison to the existing algorithms.\",\"PeriodicalId\":330528,\"journal\":{\"name\":\"2018 IEEE Statistical Signal Processing Workshop (SSP)\",\"volume\":\"17 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 Statistical Signal Processing Workshop (SSP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SSP.2018.8450847\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE Statistical Signal Processing Workshop (SSP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SSP.2018.8450847","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Shape Parameter Estimation for K-Distribution Using Variational Bayesian Approach
The sea clutter component in some of the radar and sonar signal models can be statistically characterized as following a K-distribution. This distribution has a shape parameter that is directly related to the number of scatterers. Hence, the estimation of this shape parameter is an important problem and is traditionally addressed using the maximum likelihood (ML), the method of moments (MoM) and their variants. A shortcoming of these methods is lesser accuracy in comparison to the theoretical CRB. In this paper, a variational Bayesian algorithm is proposed that provides both improved convergence and superior accuracy in comparison to the existing algorithms.
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