Localization for mixed near-field and far-field sources using data supported optimization
Tay, Wee Peng
Date of Issue2012
International Conference on Information Fusion (15th : 2012 : Singapore)
School of Electrical and Electronic Engineering
Recently, 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.
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