关键词: K最近邻法, 室内定位, 箱形图, 标准偏差, 缩放权重, 定位精度

Abstract: Since the complexity of the indoor environment and equal signal differences were assumed to equal physical distances in the traditional K nearest neighbor approach(KNN), a new access point (AP) selection method and KNN indoor location algorithm based on scaling weight were proposed. Firstly, an improved APs selection method used the box plot to filter RSS outliers and created a fingerprint database; the APs with the high loss rate in the fingerprint database were removed; the standard deviationwas used to analyze the RSS change, and select the TOP-N APs with less interference. Secondly, the scaling weight was introduced into the traditional KNN algorithm to construct a scaling weight model based on RSS. Finally, the algorithm calculated the coordinates of the first K reference points which obtained the minimum effective signal distance, and got the unknown position coordinates. In the localization simulation experiments, the mean of error distance by improved AP selection method is 1.28 times better than that by KNN. The mean of error distance by the algorithm which introduced scaling weight is 1.82 meters, it is 2.15 times better than that by KNN. The experiment results show that the positioning accuracy of the algorithm has been significantly improved in the indoor environment.

Key words: K nearest neighbor approach, indoor positioning, box plot, standard deviation, scaling weight, positioning accuracy