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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Bibliographic Details
Published in:IEEE transactions on vehicular technology 2005-05, Vol.54 (3), p.1037-1041
Main Authors: Sun, G., Guo, W.
Format: Article
Language:English
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
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Summary: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.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2005.844676