TY - GEN
T1 - An indoor positioning algorithm using joint information entropy based on WLAN fingerprint
AU - Zou, Gui
AU - Ma, Lin
AU - Zhang, Zhongzhao
AU - Mo, Yun
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/11/19
Y1 - 2014/11/19
N2 - Indoor positioning system in wireless local area network (WLAN) has become more and more popular accompanied by the popularization of GPS. While increasing numbers of access point (AP) enhancing the positioning accuracy little, the complexity increases a lot. To keep balance between the positioning accuracy and the complexity, we summarize three clustering methods named K-means, affinity propagation and fussy c means (FCM). In addition, a novel AP selection method named combination of information gain and mutual information entropy is proposed to decrease the computation cost. To get a higher positioning accuracy, we contrast two fine location methods named K nearest neighbor (KNN) and weighed K nearest neighbor (WKNN). The experiment results indicate that, the positioning accuracy within 2m is improved by using mutual information entropy and WKNN methods. By using cluster method and AP selection, we decrease the computation cost in the online phase a lot.
AB - Indoor positioning system in wireless local area network (WLAN) has become more and more popular accompanied by the popularization of GPS. While increasing numbers of access point (AP) enhancing the positioning accuracy little, the complexity increases a lot. To keep balance between the positioning accuracy and the complexity, we summarize three clustering methods named K-means, affinity propagation and fussy c means (FCM). In addition, a novel AP selection method named combination of information gain and mutual information entropy is proposed to decrease the computation cost. To get a higher positioning accuracy, we contrast two fine location methods named K nearest neighbor (KNN) and weighed K nearest neighbor (WKNN). The experiment results indicate that, the positioning accuracy within 2m is improved by using mutual information entropy and WKNN methods. By using cluster method and AP selection, we decrease the computation cost in the online phase a lot.
KW - KNN/WKNN
KW - WALN indoor positioning
KW - clustering methods
KW - information gain
KW - mutual information entropy
UR - https://www.scopus.com/pages/publications/84915751213
U2 - 10.1109/ICCCNT.2014.6963033
DO - 10.1109/ICCCNT.2014.6963033
M3 - 会议稿件
AN - SCOPUS:84915751213
T3 - 5th International Conference on Computing Communication and Networking Technologies, ICCCNT 2014
BT - 5th International Conference on Computing Communication and Networking Technologies, ICCCNT 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Conference on Computing Communication and Networking Technologies, ICCCNT 2014
Y2 - 11 July 2014 through 13 July 2014
ER -