Current localization techniques in outdoors cannot work well in indoors. Wi-Fi fingerprinting technique is an emerging localization technique for indoor environments. However in this technique, the dynamic nature of WiFi signals affects the accuracy of the measurements. In this paper, we use affinity propagation clustering method to decrease the computation complexity in location estimation. Then, we use the least variance of Received Signal Strength (RSS) measured among Access Points (APs) in each cluster. Also we assign lower weights to altering APs for each point in a cluster, to represent the level of similarity to Test Point (TP) by considering the dynamic nature of signals in indoor environments. A method for updating the radio map and improving the results is then proposed to decrease the cost of constructing the radio map. Simulation results show that the proposed method has 22.5% improvement in average in localization results, considering one altering AP in the layout, compared to the case when only RSS subset sampling is considered for localization because of altering APs.

当前在室外的定位技术在室内不能很好地工作。Wi-Fi指纹技术是一种针对室内环境的新兴定位技术。但是，在此技术中，WiFi信号的动态特性会影响测量的准确性。在本文中，我们使用亲和力传播聚类方法来降低位置估计中的计算复杂度。然后，我们使用每个群集中的接入点（AP）之间测得的接收信号强度（RSS）的最小方差。另外，我们通过考虑室内环境中信号的动态特性，为群集中每个点分配较低的权重来更改AP，以表示与测试点（TP）的相似度。然后提出了一种更新无线电地图并改善结果的方法，以降低建造无线电地图的成本。