基于改进AP选择和K最近邻法算法的室内定位技术

Indoor positioning technology based on improved access point selection and K nearest neighbor algorithm

针对复杂的室内环境和在传统K最近邻法(KNN)算法中认为信号差相等时物理距离就相等两个问题,提出了一种新的接入点(AP)选择方法和基于缩放权重的KNN室内定位算法。首先,改进AP的选择方法,使用箱形图过滤接收信号强度(RSS)的异常值,初步建立指纹库,剔除指纹库中丢失率高的AP,使用标准偏差分析RSS的变化,选择干扰较小的前n个AP;其次,在传统的KNN算法中引入缩放权重,构建一个基于RSS的缩放权重模型;最后,计算出获得最小有效信号距离的前K个参考点坐标,得到未知位置坐标。定位仿真实验中,仅对AP选择方法进行改进的算法平均定位误差比传统的KNN算法降低了21.9%,引入缩放权重算法的平均定位误差为1.82 m,比传统KNN降低了53.6%。

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.