DocumentCode :
2039546
Title :
A SVM approach to UWB-IR based positioning under NLOS conditions
Author :
Al Afif, J. ; Seong, Lim Khoon ; Krishnan, Sivanand
Author_Institution :
Inst. for Infocomm Res., A*STAR, Singapore, Singapore
fYear :
2010
fDate :
17-19 Nov. 2010
Firstpage :
233
Lastpage :
237
Abstract :
This paper presents a machine learning approach, namely the Support Vector Machine (SVM), to solve a particular localization problem. The problem is to ascertain whether an object carrying a localization tag is inside or outside a particular area. As the area becomes smaller and as the object approaches the boundaries of the area, even minute errors can result in a completely wrong estimation. SVM was chosen for this problem due to its generalization capability in handling noisy data. Training and test data for the SVM were obtained from an experimental setup of the test scenario. The results obtained proved that SVM was a suitable tool for this application, due to its ability in handling the noisy data caused by the NLOS condition.
Keywords :
support vector machines; telecommunication computing; ultra wideband communication; NLOS conditions; SVM approach; UWB-IR based positioning; impulse radio; localization problem; localization tag; machine learning; support vector machine; Classification algorithms; Kernel; Machine learning algorithms; Optimization; Support vector machines; Training; Training data; Localization; Machine Learning; Positioning; SVM; UWB-IR;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Communication Systems (ICCS), 2010 IEEE International Conference on
Conference_Location :
Singapor
Print_ISBN :
978-1-4244-7004-4
Type :
conf
DOI :
10.1109/ICCS.2010.5686095
Filename :
5686095
Link To Document :
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