TY - GEN
T1 - The indoor positioning algorithm research based on improved location fingerprinting
AU - Xia, Mingzhe
AU - Chen, Jiabin
AU - Song, Chunlei
AU - Nan, Li
AU - Kong, Chen
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/7/17
Y1 - 2015/7/17
N2 - It is the key point of the final precise of positioning that whether the positioning fingerprint database created by location fingerprinting can accurately reflect the mapping relationship between the position and the fingerprints signal. In order to improve the accuracy of indoor positioning, the mean smoothing algorithm is used to process the collected data during the building of WLAN indoor fingerprint database rather than mean value. Eliminating the gross error is necessary before processing data with mean smoothing algorithm. Meanwhile, this paper proposes an improved KNN algorithm, which is to weigh the difference of the test point and the reference point, then choose the appropriate value ofα. The algorithm is based on the constructing indoor wireless network with wireless routers and collecting the signal strength of the five wireless routers. Through the comparison with the accuracy of the commonly used indoor positioning algorithms, the results show that the positioning accuracy of the error distance within 3.6m can reach 90%, and within 4.8m can reach 97%.
AB - It is the key point of the final precise of positioning that whether the positioning fingerprint database created by location fingerprinting can accurately reflect the mapping relationship between the position and the fingerprints signal. In order to improve the accuracy of indoor positioning, the mean smoothing algorithm is used to process the collected data during the building of WLAN indoor fingerprint database rather than mean value. Eliminating the gross error is necessary before processing data with mean smoothing algorithm. Meanwhile, this paper proposes an improved KNN algorithm, which is to weigh the difference of the test point and the reference point, then choose the appropriate value ofα. The algorithm is based on the constructing indoor wireless network with wireless routers and collecting the signal strength of the five wireless routers. Through the comparison with the accuracy of the commonly used indoor positioning algorithms, the results show that the positioning accuracy of the error distance within 3.6m can reach 90%, and within 4.8m can reach 97%.
KW - Indoor positioning
KW - improved KNN algorithm
KW - location fingerprinting
KW - mean smoothing algorithm
UR - http://www.scopus.com/inward/record.url?scp=84945587202&partnerID=8YFLogxK
U2 - 10.1109/CCDC.2015.7161827
DO - 10.1109/CCDC.2015.7161827
M3 - Conference contribution
AN - SCOPUS:84945587202
T3 - Proceedings of the 2015 27th Chinese Control and Decision Conference, CCDC 2015
SP - 5736
EP - 5739
BT - Proceedings of the 2015 27th Chinese Control and Decision Conference, CCDC 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 27th Chinese Control and Decision Conference, CCDC 2015
Y2 - 23 May 2015 through 25 May 2015
ER -