一种改进的基于KNN的动态预测指纹定位算法  

Improved dynamic prediction fingerprint localization algorithm based on KNN

作　　者：卢选民[1] 院文乐 邱杨[1] 杨帆[1] 

机构地区：[1]西北工业大学电子信息学院,西安710072

出　　处：《计算机应用研究》2017年第7期2016-2018,共3页Application Research of Computers

摘　　要：针对目前的Wi-Fi室内定位普遍存在定位精度不高、定位结果不稳定等问题进行了研究,为了改善这些不稳定因素,根据室内传播信号波动较大的特点,提出了一种基于KNN的指纹定位改进算法的方法。该算法通过动态预测节点位置,从无线地图中过滤掉到标签处没有相似RSS向量的RP来寻找最近邻,以降低KNN算法的时间和计算复杂度。实验结果表明,改进后的算法在定位精确度方面有了较大的提高。因此得出结论:改进后的KNN定位算法减小了位置漂移和定位的平均误差,确实可以提高定位的精确度。Because there are some problems in Wi-Fi indoor positioning system such as the low positioning accuracy and the instability positioning results, in order to improve these unstable factors, the paper deeply studied KNN fingerprint localization algorithm and improved the algorithm according to the characteristics of signal propagation volatile in the indoor environment. The algorithm found the nearest neighbor through dynamically predicting node position and filtering out the RP without similarity RSS vector at labels from wireless map in order to reduce time and computational complexity of the algorithm KNN. The experimental results show that the improved algorithm has been greatly improved in terms of location accuracy. Therefore, it is concluded that the improved KNN algorithm can improve the accuracy of positioning, which can reduce the mean error of position drift and positioning.

关 键 词：室内定位 K最近邻 Wi-Fi定位 指纹定位 安卓 RSS向量 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]