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dc.contributor.authorWen, Fuxien
dc.contributor.authorTay, Wee Pengen
dc.identifier.citationWen, F., & Tay, W. P. (2012). Localization for mixed near-field and far-field sources using data supported optimization. 2012 15th International Conference on Information Fusion (FUSION), Singapore, pp.402-407.en
dc.description.abstractRecently, localization for the coexistence of the far-field and near-field sources has received more attentions. In this paper, a maximum likelihood (ML) localization method using data supported optimization is considered. The range and direction of arrival (DOA) of the sources are estimated sequentially. Since a two step estimation method is used, the proposed method is applicable for the near-field sources, far-field sources or the mixture of these two kinds of sources. Furthermore, the proposed method is applicable for far-field and near-field source classification. Simulations are implemented to verify the performance of the proposed method.en
dc.rights© 2012 ISIF. This paper was published in 15th International Conference on Information Fusion (FUSION) and is made available as an electronic reprint (preprint) with permission of ISIF. The paper can be found at the following official URL: []. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law.en
dc.titleLocalization for mixed near-field and far-field sources using data supported optimizationen
dc.typeConference Paperen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen
dc.contributor.conferenceInternational Conference on Information Fusion (15th : 2012 : Singapore)en
dc.description.versionPublished versionen
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