%0 Journal Article %A HOU Yue %A LI Xinchun %T Indoor positioning technology based on improved access point selection and K nearest neighbor algorithm %D 2017 %R 10.11772/j.issn.1001-9081.2017.11.3276 %J Journal of Computer Applications %P 3276-3280 %V 37 %N 11 %X Since indoor environment is complex and equal signal differences are assumed to equal physical distances in the traditional K Nearest Neighbor (KNN) approach, a new Access Point (AP) selection method and KNN indoor positioning algorithm based on scaling weight were proposed. Firstly, in the improved AP selection method, box plot was used to filter Received Signal Strength (RSS) outliers and create a fingerprint database. The AP with high loss rate in the fingerprint database were removed. The standard deviation was used to analyze the variations of RSS, and TOP-N APs with less interference were selected. Secondly, the scaling weight was introduced into the traditional KNN algorithm to construct a scaling weight model based on RSS. Finally, the first K reference points which obtained the minimum effective signal distance were calculated to get the unknown position coordinates. In the localization simulation experiments, the mean of error distance by improved AP selection method is 21.9% lower than that by KNN. The mean of error distance by the algorithm which introduced scaling weight is 1.82 m, which is 53.6% lower than that by KNN. %U http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2017.11.3276