基于K-means^(++)的WiFi指纹定位算法

Research on WiFi fingerprint localization algorithm based on K-means^(++)

针对接收信号强度(RSS)的波动性、易受干扰等特征,在匹配算法时易被较远处参考点所干扰,导致定位结果偏差较大,提出了一种基于聚类结果的指纹定位算法。与传统的按参考点处信号强度进行聚类不同,采取了利用K-means^(++)算法按参考点位置聚类的方法。在聚类的结果上,选择RSS均值最大的M个AP,使用有监督的学习算法KNN对测试点进行分类,再用确定性的匹配算法加权K最近邻居法来计算位置坐标。实验结果表明:所提方法在定位性能上比按信号强度聚类的方法有较大提高。

Aiming at characteristics of received signal strength(RSS),such as volatility,susceptibility to interference and problem that it is easy to be disturbed by the remote reference point(RP)in the algorithm matching process,and resulting in a large deviation of the finalized positioning results,a fingerprint localization algorithm based on clustering results is proposed.Different from traditional clustering by reference point signal strength,K-means++algorithm by reference points location clustering is used.Select M APs(max mean,MM)with the largest RSS mean,and using the supervised learning algorithm,KNN,to classify the test points,location coordinates can be calculated by using the deterministic matching algorithm weighted K-nearest neighbor(WKNN).The experimental results show that the proposed method greatly improve the localization characteristics than the method of clustering by signal strength.