{"title":"基于传感器网络的随机信号源定位研究","authors":"Ashok Sundaresan, P. Varshney, N. Rao","doi":"10.1109/CISS.2009.5054722","DOIUrl":null,"url":null,"abstract":"The problem of source localization using a network of sensors is considered. A maximum likelihood estimation (MLE) based approach is adopted. The measurements received at the sensors due to the random phenomenon are spatially correlated and are characterized by a multivariate distribution. Using the theory of copulas, the joint parametric density of sensor observations is obtained assuming only the knowledge of their marginal densities. An example showing the efficiency of the proposed approach is presented.","PeriodicalId":433796,"journal":{"name":"2009 43rd Annual Conference on Information Sciences and Systems","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"On localizing the source of random signals using sensor networks\",\"authors\":\"Ashok Sundaresan, P. Varshney, N. Rao\",\"doi\":\"10.1109/CISS.2009.5054722\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The problem of source localization using a network of sensors is considered. A maximum likelihood estimation (MLE) based approach is adopted. The measurements received at the sensors due to the random phenomenon are spatially correlated and are characterized by a multivariate distribution. Using the theory of copulas, the joint parametric density of sensor observations is obtained assuming only the knowledge of their marginal densities. An example showing the efficiency of the proposed approach is presented.\",\"PeriodicalId\":433796,\"journal\":{\"name\":\"2009 43rd Annual Conference on Information Sciences and Systems\",\"volume\":\"2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-03-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 43rd Annual Conference on Information Sciences and Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CISS.2009.5054722\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 43rd Annual Conference on Information Sciences and Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CISS.2009.5054722","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
On localizing the source of random signals using sensor networks
The problem of source localization using a network of sensors is considered. A maximum likelihood estimation (MLE) based approach is adopted. The measurements received at the sensors due to the random phenomenon are spatially correlated and are characterized by a multivariate distribution. Using the theory of copulas, the joint parametric density of sensor observations is obtained assuming only the knowledge of their marginal densities. An example showing the efficiency of the proposed approach is presented.
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