WiFi指纹定位中改进的加权k近邻算法

Improved weighted k-nearest neighbor algorithm for WiFi fingerprint positioning

针对WiFi指纹定位中传统的信号欧氏距离不能很好地反映各位置点间物理距离的问题,提出了改进的加权k近邻定位算法。首先,在信号距离的计算中引入接收信号强度的方差;然后,根据接收信号强度和物理距离之间的非线性关系引入加权系数,设计了一种信号加权欧氏距离;最后,利用信号加权欧氏距离进行指纹匹配和位置估计,改进了加权k近邻算法。在真实环境下的实验结果表明,信号加权欧氏距离能够更准确地衡量各点之间的物理距离并选择更合理的最近邻参考点。与现有的加权k近邻算法相比,改进的加权k近邻算法能够明显地提高WiFi指纹定位的精度。

In WiFi fingerprint positioning, the traditional Euclidean distance of signals cannot well reflect the physical distance between position points. To solve this problem, the weighted k-nearest neighbor positioning algorithm is improved. First, the variance of received signal strength is introduced into the calculation of the signal distance, and a weighted Euclidean distance of signals is designed according to the nonlinear relationship between received signal strength and physical distance. Finally, the weighted Euclidean distance of signals is used for the fingerprint matching and the position estimation, and an improved weighted k-nearest neighbor algorithm is proposed. Experimental results in real environment show that the weighted Euclidean distance of signals can be used to measure the physical distance between points more accurately and select more reasonable nearest neighbor reference points. Compared with the existing weighted k-nearest neighbor algorithms, the improved weighted k-nearest neighbor algorithm can significantly improve the accuracy of WiFi fingerprint positioning.