A novel WIFI indoor positioning method based on Genetic Algorithm and Twin Support Vector Regression

Author

Wenhua Le ; Zhanbin Wang ; Jingcheng Wang ; Guanglei Zhao ; Haoxuan Miao

Author_Institution

Dept. of Autom., Shanghai Jiao Tong Univ., Shanghai, China

fYear

2014

fDate

May 31 2014-June 2 2014

Firstpage

4859

Lastpage

4862

Abstract

We propose a novel regression, which is called Twin Support Vector Regression (TSVR) to improve the precision of indoor positioning. Similar as Support Vector Regression (SVR), there are 6 parameters to be identified. However, compared with SVR, less computation time and approximate performance can be achieved with TSVR. Genetic Algorithm (GA) is used to avoid local optimum in indoor positioning to get proper parameters in TSVR. Experimental example is shown to illustrate the effectiveness of the proposed methods.

Keywords

Internet of Things; genetic algorithms; indoor radio; regression analysis; support vector machines; wireless LAN; GA; Internet-of-things development; TSVR; approximate performance; genetic algorithm; less computation time; novel WIFI indoor positioning method; twin support vector regression; Conferences; Fingerprint recognition; Genetic algorithms; IEEE 802.11 Standards; Interference; Kernel; Support vector machines; GA; SVR; TSVR; indoor positioning;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference (2014 CCDC), The 26th Chinese

Conference_Location

Changsha

Print_ISBN

978-1-4799-3707-3

Type

conf

DOI

10.1109/CCDC.2014.6853043

Filename

6853043