TY - GEN
T1 - Accuracy enhancement for fingerprint-based WLAN indoor probability positioning algorithm
AU - Ma, Lin
AU - Xu, Yubin
AU - Zhou, Mu
PY - 2010
Y1 - 2010
N2 - This paper proposed an accuracy enhancement for fingerprint-based WLAN indoor probability positioning algorithm. Based on Bayes' probability theory, the traditional probability positioning algorithm converts the new sample location coordinates corresponding to the posterior probability to a priori marginal probability, which leads to the reference point having the greatest product for the estimated location as the terminal coordinates. However, due to the limited quantity of recorded signal strength in the offline phase, new recorded signal strength, different from any signal strengths in the Radio map, will appear in the on-line phase, which means the reference point may not completely characterize the signal strength distribution of this point and result in a poorer poisoning accuracy Therefore, based on the accuracy of enhanced probabilistic location algorithm, this paper proposes to use Gaussian and polynomial continuous curve to realize the least square fitting for the original signal discrete intensity distribution, which achieves to improve the positioning accuracy. And also, this paper presents an experiment made in a WLAN indoor open environment with 8×9m2, and the results verify the feasibility and validity of the proposed algorithm.
AB - This paper proposed an accuracy enhancement for fingerprint-based WLAN indoor probability positioning algorithm. Based on Bayes' probability theory, the traditional probability positioning algorithm converts the new sample location coordinates corresponding to the posterior probability to a priori marginal probability, which leads to the reference point having the greatest product for the estimated location as the terminal coordinates. However, due to the limited quantity of recorded signal strength in the offline phase, new recorded signal strength, different from any signal strengths in the Radio map, will appear in the on-line phase, which means the reference point may not completely characterize the signal strength distribution of this point and result in a poorer poisoning accuracy Therefore, based on the accuracy of enhanced probabilistic location algorithm, this paper proposes to use Gaussian and polynomial continuous curve to realize the least square fitting for the original signal discrete intensity distribution, which achieves to improve the positioning accuracy. And also, this paper presents an experiment made in a WLAN indoor open environment with 8×9m2, and the results verify the feasibility and validity of the proposed algorithm.
KW - Curve fitting
KW - Fingerprint
KW - Indoor positioning
KW - Probability
KW - WLAN
UR - https://www.scopus.com/pages/publications/78650423888
U2 - 10.1109/PCSPA.2010.49
DO - 10.1109/PCSPA.2010.49
M3 - 会议稿件
AN - SCOPUS:78650423888
SN - 9780769541808
T3 - Proceedings - 2010 1st International Conference on Pervasive Computing, Signal Processing and Applications, PCSPA 2010
SP - 167
EP - 170
BT - Proceedings - 2010 1st International Conference on Pervasive Computing, Signal Processing and Applications, PCSPA 2010
T2 - 1st International Conference on Pervasive Computing, Signal Processing and Applications, PCSPA 2010
Y2 - 17 September 2010 through 19 September 2010
ER -