Cart (Loading....) | Create Account
Close category search window
 

Robust mobile geo-location algorithm based on LS-SVM

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Guolin Sun ; Sch. of Commun. & Inf. Eng., Univ. of Electron. Sci. & Technol. of China, Chengdu, China ; Wei Guo

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.

Published in:

Vehicular Technology, IEEE Transactions on  (Volume:54 ,  Issue: 3 )

Date of Publication:

May 2005

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.