基于离散系数改进的VWKNN位置指纹定位算法

位置指纹算法是研究室内定位技术的主要方法，其中在线阶段的匹配算法是影响室内定位精度的主要因素之一。目前，在线阶段的匹配算法有最近邻算法、K近邻算法以及加权K近邻算法。其中，最近邻算法和K近邻算法都没有考虑到不同参考点和待定位点之间的欧氏距离对定位精度的影响，而加权K近邻算法虽然考虑到了欧氏距离对定位精度的影响，对最终的定位结果采用欧氏距离归一化处理进行加权，却没有考虑到AP信号的波动性对定位结果也会产生很大的影响。因此，针对在线阶段的匹配算法作出改进，提出了基于离散系数改进的加权K近邻算法。在离线阶段建立位置指纹数据库，在在线阶段使用离散系数来反映各AP信号的稳定性，进而对待定位点与参考点之间的欧氏距离进行加权，计算出所有的加权欧氏距离后，从中选取距离最近的k个参考点，估算出待定位点的物理位置。实验结果表明:基于离散系数改进的加权K近邻算法可以实现平均定位精度比K近邻算法提高15%~17%，较加权K近邻算法提高了11%~13%的定位效果。 

VWKNN location fingerprint positioning algorithm based on improved discrete coefficient

The location fingerprint algorithm is the main method to study the indoor positioning technology, and the online matching algorithm is one of the main factors affecting the indoor positioning accuracy. At present, the matching algorithms in online stage include the nearest neighbor algorithm, K-nearest neighbor algorithm and weighted K-nearest neighbor algorithm. However, these three algorithms do not take into account the influence of the fluctuation of AP signal on the positioning result. In order to improve the matching algorithm in online stage, a weighted K-nearest neighbor algorithm based on the improved discrete coefficient is proposed. In offline stage the purpose is to establish a fingerprint database, in the online stage using discrete coefficient to reflect the stability of the various AP signal and treat the anchor point with weighted Euclidean distance between the reference point, calculate all the weighted Euclidean distance, choose the nearest k reference points, so as to estimate the physical location of pending sites. Finally, experiments show that the weighted K-nearest neighbor algorithm based on the improved discrete coefficient can achieve an average positioning accuracy which is 15%-17% higher than the K-nearest neighbor algorithm and 11%-13% higher than the weighted K-nearest neighbor algorithm.