Localization for mixed near-field and far-field sources using data supported optimization

• Main Authors: Wen, Fuxi, Tay, Wee Peng
• Other Authors: School of Electrical and Electronic Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2013
• Online Access: https://hdl.handle.net/10356/96421; http://hdl.handle.net/10220/10631; http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6289831&url=http%3A%2F%2Fieeexplore.ieee.org%2Fiel5%2F6269381%2F6289713%2F06289831.pdf%3Farnumber%3D6289831
• Institution: Nanyang Technological University

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.