Kang Li, Jinghua Li, Yutao Jiao, Guoru Ding, Shihua Dong
{"title":"基于高斯混合噪声分析的RSS目标定位期望最大化解","authors":"Kang Li, Jinghua Li, Yutao Jiao, Guoru Ding, Shihua Dong","doi":"10.1117/12.2589432","DOIUrl":null,"url":null,"abstract":"RSS-based target localization algorithms are usually derived from channel path-loss model where the measurement noise is generally assumed to obey Gaussian distribution. In this paper, we approximate the realistic measurement noise distribution by a Gaussian mixture model and proposed an improved mixture noise analysis-based RSS target localization algorithm employing expectation maximization, called Gaussian mixture-expectation maximization (GMEM) approach, to estimate target coordinates iteratively, which can be efficiently used for tackling unknown parameters of maximum likelihood estimation and non-convex optimization. Simulations show a considerable performance gain of our proposed localization algorithm in 2-D wireless sensor network.","PeriodicalId":415097,"journal":{"name":"International Conference on Signal Processing Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"An expectation maximization solution for RSS target localization by Gaussian mixture noise analysis\",\"authors\":\"Kang Li, Jinghua Li, Yutao Jiao, Guoru Ding, Shihua Dong\",\"doi\":\"10.1117/12.2589432\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"RSS-based target localization algorithms are usually derived from channel path-loss model where the measurement noise is generally assumed to obey Gaussian distribution. In this paper, we approximate the realistic measurement noise distribution by a Gaussian mixture model and proposed an improved mixture noise analysis-based RSS target localization algorithm employing expectation maximization, called Gaussian mixture-expectation maximization (GMEM) approach, to estimate target coordinates iteratively, which can be efficiently used for tackling unknown parameters of maximum likelihood estimation and non-convex optimization. Simulations show a considerable performance gain of our proposed localization algorithm in 2-D wireless sensor network.\",\"PeriodicalId\":415097,\"journal\":{\"name\":\"International Conference on Signal Processing Systems\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-01-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Conference on Signal Processing Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1117/12.2589432\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Signal Processing Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1117/12.2589432","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An expectation maximization solution for RSS target localization by Gaussian mixture noise analysis
RSS-based target localization algorithms are usually derived from channel path-loss model where the measurement noise is generally assumed to obey Gaussian distribution. In this paper, we approximate the realistic measurement noise distribution by a Gaussian mixture model and proposed an improved mixture noise analysis-based RSS target localization algorithm employing expectation maximization, called Gaussian mixture-expectation maximization (GMEM) approach, to estimate target coordinates iteratively, which can be efficiently used for tackling unknown parameters of maximum likelihood estimation and non-convex optimization. Simulations show a considerable performance gain of our proposed localization algorithm in 2-D wireless sensor network.