Localization for mixed near-field and far-field sources using data supported optimization
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 sequent...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
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2013
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| 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 |
| _version_ | 1811689124689608704 |
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| author | Wen, Fuxi Tay, Wee Peng |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Wen, Fuxi Tay, Wee Peng |
| author_sort | Wen, Fuxi |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T05:43:07Z |
| format | Conference Paper |
| id | ntu-10356/96421 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:43:07Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/964212019-12-06T19:30:28Z Localization for mixed near-field and far-field sources using data supported optimization Wen, Fuxi Tay, Wee Peng School of Electrical and Electronic Engineering International Conference on Information Fusion (15th : 2012 : Singapore) 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. Published version 2013-06-25T06:23:18Z 2019-12-06T19:30:28Z 2013-06-25T06:23:18Z 2019-12-06T19:30:28Z 2012 2012 Conference Paper Wen, 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. 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 en © 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: [http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6289831&url=http%3A%2F%2Fieeexplore.ieee.org%2Fiel5%2F6269381%2F6289713%2F06289831.pdf%3Farnumber%3D6289831]. 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. application/pdf |
| spellingShingle | Wen, Fuxi Tay, Wee Peng Localization for mixed near-field and far-field sources using data supported optimization |
| title | Localization for mixed near-field and far-field sources using data supported optimization |
| title_full | Localization for mixed near-field and far-field sources using data supported optimization |
| title_fullStr | Localization for mixed near-field and far-field sources using data supported optimization |
| title_full_unstemmed | Localization for mixed near-field and far-field sources using data supported optimization |
| title_short | Localization for mixed near-field and far-field sources using data supported optimization |
| title_sort | localization for mixed near field and far field sources using data supported optimization |
| url | 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 |
| work_keys_str_mv | AT wenfuxi localizationformixednearfieldandfarfieldsourcesusingdatasupportedoptimization AT tayweepeng localizationformixednearfieldandfarfieldsourcesusingdatasupportedoptimization |