基于WiFi的自适应匹配预处理WKNN算法

针对基于接收信号强度（Received Signal Strength，RSS）的WiFi室内定位技术中,传统加权K邻近（Weighted K-nearest Neighbor，WKNN）算法不能自适应获取WLAN中有效接入点（Acess Point，AP）且参考点匹配准确度不高的问题，本文提出了自适应匹配预处理WKNN算法。该算法中每个实时定位点自适应地根据网络状况对AP的RSS均值由大到小排序，然后选择RSS均值较大的前M个AP，与参考点中对应的M个AP一起参与匹配预处理计算，从而优化了传统的指纹定位算法。同时将室内定位和室内地图相结合，使参考点和定位结果直观地展示在地图上，并通过使用地图数据大幅度简化了离线训练过程。此外，本文设计并实现了基于Android平台的室内定位系统，通过该系统验证了本文所提算法在单点定位和移动定位中的有效性。实验结果表明，该算法可获得30%以上的定位误差改善，有效提高了定位精度和定位稳定性。 

WiFi-based Self-adaptive Matching and Preprocessing WKNN Algorithm

In WiFi-based indoor positioning technology, the traditional Weighed K-nearest Neighbor (WKNN) algorithm based on received signal strength (RSS) is not able to adaptively get valid access points (AP) as well as relatively high accuracy of reference point matching. Aiming at this problem, we propose WKNN algorithm with self-adaptive matching and preprocessing function in this paper. In this algorithm, the RSS values of AP are sorted according to the network state adaptively, and the top M AP coupled with the corresponding M AP in the reference points are selected to participate in the calculation of the matching in order to optimize the traditional fingerprint positioning algorithm. At the same time, indoor positioning and indoor maps are combined to make the reference point and location results visually displayed on the map, and the offline training process is simplified by using the map data. In addition, the indoor positioning system based on Android platform is designed and realized. The effectiveness of the proposed algorithm in single point positioning and mobile positioning is verified by this system. Experimental results show that the proposed algorithm can achieve more than 30% of the positioning error improvement which can effectively improve the positioning accuracy and positioning stability.