{"title":"Deterministic maximum likelihood method for the localization of near-field sources: algorithm and performance analysis","authors":"Erdinç Çekli, H. A. Çırpan","doi":"10.1109/ICECS.2001.957683","DOIUrl":null,"url":null,"abstract":"A deterministic maximum likelihood localization algorithm is adapted to estimate the direction of arrival and range parameters of the near field sources. Since the direct maximum likelihood estimation of near-field source parameters results in complicated multi-parameter optimization problems, we reformulated the estimation problem in terms of actual-data sample, called the incomplete data and a hypothetical data set, called the complete data and then devised the expectation/maximization iterative method for obtaining maximum likelihood estimates. The performance analysis of the proposed algorithm is then carried out through the evaluation of Cramer-Rao bounds. Finally, some simulation results are presented.","PeriodicalId":141392,"journal":{"name":"ICECS 2001. 8th IEEE International Conference on Electronics, Circuits and Systems (Cat. No.01EX483)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2001-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ICECS 2001. 8th IEEE International Conference on Electronics, Circuits and Systems (Cat. No.01EX483)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICECS.2001.957683","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7
Abstract
A deterministic maximum likelihood localization algorithm is adapted to estimate the direction of arrival and range parameters of the near field sources. Since the direct maximum likelihood estimation of near-field source parameters results in complicated multi-parameter optimization problems, we reformulated the estimation problem in terms of actual-data sample, called the incomplete data and a hypothetical data set, called the complete data and then devised the expectation/maximization iterative method for obtaining maximum likelihood estimates. The performance analysis of the proposed algorithm is then carried out through the evaluation of Cramer-Rao bounds. Finally, some simulation results are presented.