针对KNN指纹定位算法定位耗时长和基于K-Means聚类的KNN指纹定位算法定位精度不稳定的问题,本文提出了一种以接入点为离散点生成泰森多边形,利用泰森多边形对指纹聚类,然后使用最强接入点法确定移动节点的定位区域,最后通过动态KNN算法进行定位的指纹聚类定位算法.实验表明,该算法能有效缩短定位时间并提高定位精度,在接入点数量变化时表现出较好的定位性能,且在不同定位区域中性能具有较好的普适性.%To solve the problems of long position time-consuming in KNN fingerprint localization algorithm and un-stable accuracy in K-Means clustering based localization algorithm,a novel fingerprint clustering localization algo-rithm is proposed. This algorithm considers APs as Voronoi diagram's generators to create Voronoi cells,uses these cells to cluster fingerprints of database and a method based on the biggest received signal strength to find out the po-sitioning subarea of mobile nodes,and estimates the location of mobile node by automatic KNN algorithm. Experi-ment results reveal that this algorithm sharply reduces position time-consuming,improves the accuracy,and has a good performance when the quantity of AP changes. With the location area altering the performance still exists.
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