TY - GEN
T1 - Space division and dimensional reduction methods for indoor positioning system
AU - Mo, Yun
AU - Zhang, Zhongzhao
AU - Meng, Weixiao
AU - Agha, Gul A
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/9/9
Y1 - 2015/9/9
N2 - With the popularity of smart phones and the development of mobile computing, indoor positioning services have triggered large scale of technology innovation and commercial cooperation. In the field of Wi-Fi based fingerprinting positioning system, for one thing, we deploy space division method based on Random Forest for dividing the fingerprinting radio map into sub regions freely and classifying candidate points accurately. For another thing, we propose a dimension reduction method, which integrates Maximum Likelihood Estimation for estimating intrinsic dimensionality and Kernel Principal Component Analysis for feature extraction, to tremendously reduce the size of a radio map, thereby saving terminal storage and alleviating error margin. Compared with linear feature extraction methods and manifold learning techniques, the proposed method shows a better performance in low dimension. The experimental results demonstrate that the proposed indoor positioning system, which is based on the given space division and dimension reduction techniques, could achieve 98% space division accuracy, 85% confidence probability with 2m positioning error and reduce 74% size of the radio map in addition to its noise suppression ability.
AB - With the popularity of smart phones and the development of mobile computing, indoor positioning services have triggered large scale of technology innovation and commercial cooperation. In the field of Wi-Fi based fingerprinting positioning system, for one thing, we deploy space division method based on Random Forest for dividing the fingerprinting radio map into sub regions freely and classifying candidate points accurately. For another thing, we propose a dimension reduction method, which integrates Maximum Likelihood Estimation for estimating intrinsic dimensionality and Kernel Principal Component Analysis for feature extraction, to tremendously reduce the size of a radio map, thereby saving terminal storage and alleviating error margin. Compared with linear feature extraction methods and manifold learning techniques, the proposed method shows a better performance in low dimension. The experimental results demonstrate that the proposed indoor positioning system, which is based on the given space division and dimension reduction techniques, could achieve 98% space division accuracy, 85% confidence probability with 2m positioning error and reduce 74% size of the radio map in addition to its noise suppression ability.
KW - KPCA
KW - MLE
KW - RF
KW - indoor positioning
UR - http://www.scopus.com/inward/record.url?scp=84953706293&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84953706293&partnerID=8YFLogxK
U2 - 10.1109/ICC.2015.7248827
DO - 10.1109/ICC.2015.7248827
M3 - Conference contribution
AN - SCOPUS:84953706293
T3 - IEEE International Conference on Communications
SP - 3263
EP - 3268
BT - 2015 IEEE International Conference on Communications, ICC 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE International Conference on Communications, ICC 2015
Y2 - 8 June 2015 through 12 June 2015
ER -