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Robust mobile geo-location algorithm based on LS-SVM
Support vector machine (SVM) is powerful to solve problems such as nonlinear classification, function estimation and density estimation. It has also led to many other recent developments in kernel-based learning fields. In this paper, we extend a high-accuracy, real-time, and fault-tolerant SVM to m...
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| Published in: | IEEE transactions on vehicular technology 2005-05, Vol.54 (3), p.1037-1041 |
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| Main Authors: | , |
| Format: | Article |
| Language: | English |
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| cited_by | cdi_FETCH-LOGICAL-c381t-d27b7bf7a0bde74010738b7c8460cc7cd7dbd42ec3bba9826568b05e7001f6513 |
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| cites | cdi_FETCH-LOGICAL-c381t-d27b7bf7a0bde74010738b7c8460cc7cd7dbd42ec3bba9826568b05e7001f6513 |
| container_end_page | 1041 |
| container_issue | 3 |
| container_start_page | 1037 |
| container_title | IEEE transactions on vehicular technology |
| container_volume | 54 |
| creator | Sun, G. Guo, W. |
| description | Support vector machine (SVM) is powerful to solve problems such as nonlinear classification, function estimation and density estimation. It has also led to many other recent developments in kernel-based learning fields. In this paper, we extend a high-accuracy, real-time, and fault-tolerant SVM to mobile geo-location problem, which has become an important component of pervasive computing. Simulation results show its basic location performance superior to conventional least square (LS) algorithm especially under nonlight of sight (NLOS) environments. Finally, we also analyze the impacts of training samples and training area on test location accuracy. |
| doi_str_mv | 10.1109/TVT.2005.844676 |
| format | article |
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| fulltext | fulltext |
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| ispartof | IEEE transactions on vehicular technology, 2005-05, Vol.54 (3), p.1037-1041 |
| issn | 0018-9545 1939-9359 |
| language | eng |
| recordid | cdi_crossref_primary_10_1109_TVT_2005_844676 |
| source | IEEE Electronic Library (IEL) Journals |
| subjects | Algorithms Applied sciences Base stations Cellular networks Density Equipments and installations Exact sciences and technology Fault tolerance FCC Geometry Least square (LS) support vector machine (SVM) Least squares method Mathematical analysis Mobile computing mobile geo-location Mobile radiocommunication systems Neural networks nonlight of sight (NLOS) Position (location) Radiocommunications Robustness Support vector machine classification Support vector machines Telecommunications Telecommunications and information theory Training Wireless networks |
| title | Robust mobile geo-location algorithm based on LS-SVM |
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